{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 采用xgboost模型完成商品分类\n",
    "数据集 Rent Listing Inqueries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "import xgboost as xgb\n",
    "from xgboost import XGBClassifier\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 准备数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>price</th>\n",
       "      <th>price_bathrooms</th>\n",
       "      <th>price_bedrooms</th>\n",
       "      <th>room_diff</th>\n",
       "      <th>room_num</th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>Day</th>\n",
       "      <th>...</th>\n",
       "      <th>walk</th>\n",
       "      <th>walls</th>\n",
       "      <th>war</th>\n",
       "      <th>washer</th>\n",
       "      <th>water</th>\n",
       "      <th>wheelchair</th>\n",
       "      <th>wifi</th>\n",
       "      <th>windows</th>\n",
       "      <th>work</th>\n",
       "      <th>interest_level</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.5</td>\n",
       "      <td>3</td>\n",
       "      <td>3000</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>750.000000</td>\n",
       "      <td>-1.5</td>\n",
       "      <td>4.5</td>\n",
       "      <td>2016</td>\n",
       "      <td>6</td>\n",
       "      <td>24</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>5465</td>\n",
       "      <td>2732.5</td>\n",
       "      <td>1821.666667</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2016</td>\n",
       "      <td>6</td>\n",
       "      <td>12</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2850</td>\n",
       "      <td>1425.0</td>\n",
       "      <td>1425.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2016</td>\n",
       "      <td>4</td>\n",
       "      <td>17</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3275</td>\n",
       "      <td>1637.5</td>\n",
       "      <td>1637.500000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2016</td>\n",
       "      <td>4</td>\n",
       "      <td>18</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>4</td>\n",
       "      <td>3350</td>\n",
       "      <td>1675.0</td>\n",
       "      <td>670.000000</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2016</td>\n",
       "      <td>4</td>\n",
       "      <td>28</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 228 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   bathrooms  bedrooms  price  price_bathrooms  price_bedrooms  room_diff  \\\n",
       "0        1.5         3   3000           1200.0      750.000000       -1.5   \n",
       "1        1.0         2   5465           2732.5     1821.666667       -1.0   \n",
       "2        1.0         1   2850           1425.0     1425.000000        0.0   \n",
       "3        1.0         1   3275           1637.5     1637.500000        0.0   \n",
       "4        1.0         4   3350           1675.0      670.000000       -3.0   \n",
       "\n",
       "   room_num  Year  Month  Day       ...        walk  walls  war  washer  \\\n",
       "0       4.5  2016      6   24       ...           0      0    0       0   \n",
       "1       3.0  2016      6   12       ...           0      0    0       0   \n",
       "2       2.0  2016      4   17       ...           0      0    0       0   \n",
       "3       2.0  2016      4   18       ...           0      0    0       0   \n",
       "4       5.0  2016      4   28       ...           0      0    1       0   \n",
       "\n",
       "   water  wheelchair  wifi  windows  work  interest_level  \n",
       "0      0           0     0        0     0               1  \n",
       "1      0           0     0        0     0               2  \n",
       "2      0           0     0        0     0               0  \n",
       "3      0           0     0        0     0               2  \n",
       "4      0           0     0        0     0               2  \n",
       "\n",
       "[5 rows x 228 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train = pd.read_csv('./code/data/RentListingInquries_FE_train.csv')\n",
    "test = pd.read_csv('./code/data/RentListingInquries_FE_test.csv')\n",
    "\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(49352, 228)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    34284\n",
       "1    11229\n",
       "0     3839\n",
       "Name: interest_level, dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 观察标签 interest_level 分布\n",
    "train.interest_level.value_counts()\n",
    "#train['interest_level'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 运行时间过长，采用少量数据\n",
    "y = train[:1000]['interest_level']\n",
    "X = train[:1000].drop('interest_level', axis = 1)\n",
    "\n",
    "# 分割训练数据和测试数据\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state = 21, test_size = 0.2)\n",
    "X_train.shape;\n",
    "y_train.values;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## xgboost \n",
    "**xgboost.cv**(params, dtrain, num_boost_round=10, nfold=3, stratified=False, folds=None, metrics=(), obj=None, feval=None, maximize=False, early_stopping_rounds=None, fpreproc=None, as_pandas=True, verbose_eval=None, show_stdv=True, seed=0, callbacks=None, shuffle=True)   \n",
    "class **xgboost.XGBClassifier** ( max_depth=3, learning_rate=0.1, n_estimators=100, silent=True,   \n",
    "&emsp; objective='binary:logistic', booster='gbtree', n_jobs=1, nthread=None, <br>\n",
    "&emsp; gamma=0, min_child_weight=1, max_delta_step=0, subsample=1, colsample_bytree=1,   \n",
    "&emsp; colsample_bylevel=1, reg_alpha=0, reg_lambda=1, scale_pos_weight=1, base_score=0.5,   \n",
    "&emsp; random_state=0, seed=None, missing=None, **kwargs)  \n",
    "(https://xgboost.readthedocs.io/en/latest/python/python_api.html#module-xgboost.sklearn)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. 初步确定弱学习器数目"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
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       "        text-align: right;\n",
       "    }\n",
       "\n",
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       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>test-mlogloss-mean</th>\n",
       "      <th>test-mlogloss-std</th>\n",
       "      <th>train-mlogloss-mean</th>\n",
       "      <th>train-mlogloss-std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.047559</td>\n",
       "      <td>0.005322</td>\n",
       "      <td>1.040275</td>\n",
       "      <td>0.002585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.010883</td>\n",
       "      <td>0.003960</td>\n",
       "      <td>0.988056</td>\n",
       "      <td>0.004265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.974102</td>\n",
       "      <td>0.004738</td>\n",
       "      <td>0.942376</td>\n",
       "      <td>0.004495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.943336</td>\n",
       "      <td>0.004458</td>\n",
       "      <td>0.900392</td>\n",
       "      <td>0.006483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.913940</td>\n",
       "      <td>0.004073</td>\n",
       "      <td>0.862032</td>\n",
       "      <td>0.005640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.888710</td>\n",
       "      <td>0.004376</td>\n",
       "      <td>0.829401</td>\n",
       "      <td>0.003905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.868966</td>\n",
       "      <td>0.005224</td>\n",
       "      <td>0.798906</td>\n",
       "      <td>0.003260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.850290</td>\n",
       "      <td>0.004816</td>\n",
       "      <td>0.772262</td>\n",
       "      <td>0.002969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.833376</td>\n",
       "      <td>0.003953</td>\n",
       "      <td>0.748114</td>\n",
       "      <td>0.004163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.818894</td>\n",
       "      <td>0.004849</td>\n",
       "      <td>0.726744</td>\n",
       "      <td>0.004047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.807209</td>\n",
       "      <td>0.007262</td>\n",
       "      <td>0.706485</td>\n",
       "      <td>0.005171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.796560</td>\n",
       "      <td>0.007789</td>\n",
       "      <td>0.688447</td>\n",
       "      <td>0.004824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.785693</td>\n",
       "      <td>0.008242</td>\n",
       "      <td>0.670807</td>\n",
       "      <td>0.005061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.777823</td>\n",
       "      <td>0.005168</td>\n",
       "      <td>0.652904</td>\n",
       "      <td>0.005319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.770674</td>\n",
       "      <td>0.005357</td>\n",
       "      <td>0.638080</td>\n",
       "      <td>0.004880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.762001</td>\n",
       "      <td>0.007438</td>\n",
       "      <td>0.624562</td>\n",
       "      <td>0.004929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.756956</td>\n",
       "      <td>0.006003</td>\n",
       "      <td>0.611841</td>\n",
       "      <td>0.005025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.750813</td>\n",
       "      <td>0.007029</td>\n",
       "      <td>0.598423</td>\n",
       "      <td>0.005793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.747629</td>\n",
       "      <td>0.006239</td>\n",
       "      <td>0.587672</td>\n",
       "      <td>0.005346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.743270</td>\n",
       "      <td>0.006364</td>\n",
       "      <td>0.577078</td>\n",
       "      <td>0.005047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.738120</td>\n",
       "      <td>0.007295</td>\n",
       "      <td>0.566394</td>\n",
       "      <td>0.004576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.733781</td>\n",
       "      <td>0.007588</td>\n",
       "      <td>0.556401</td>\n",
       "      <td>0.004546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.728309</td>\n",
       "      <td>0.008741</td>\n",
       "      <td>0.547351</td>\n",
       "      <td>0.004496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0.725192</td>\n",
       "      <td>0.008494</td>\n",
       "      <td>0.538262</td>\n",
       "      <td>0.003985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0.722602</td>\n",
       "      <td>0.006831</td>\n",
       "      <td>0.529843</td>\n",
       "      <td>0.003710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0.720975</td>\n",
       "      <td>0.008532</td>\n",
       "      <td>0.521189</td>\n",
       "      <td>0.003521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0.720737</td>\n",
       "      <td>0.008103</td>\n",
       "      <td>0.512666</td>\n",
       "      <td>0.003652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0.719949</td>\n",
       "      <td>0.009091</td>\n",
       "      <td>0.504758</td>\n",
       "      <td>0.004209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0.718626</td>\n",
       "      <td>0.008715</td>\n",
       "      <td>0.497604</td>\n",
       "      <td>0.004119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0.715163</td>\n",
       "      <td>0.009193</td>\n",
       "      <td>0.490549</td>\n",
       "      <td>0.003704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>970</th>\n",
       "      <td>0.996984</td>\n",
       "      <td>0.056560</td>\n",
       "      <td>0.025210</td>\n",
       "      <td>0.000298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>971</th>\n",
       "      <td>0.997045</td>\n",
       "      <td>0.056838</td>\n",
       "      <td>0.025184</td>\n",
       "      <td>0.000293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>972</th>\n",
       "      <td>0.997066</td>\n",
       "      <td>0.057347</td>\n",
       "      <td>0.025169</td>\n",
       "      <td>0.000297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>973</th>\n",
       "      <td>0.996820</td>\n",
       "      <td>0.057071</td>\n",
       "      <td>0.025152</td>\n",
       "      <td>0.000294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>974</th>\n",
       "      <td>0.996122</td>\n",
       "      <td>0.056560</td>\n",
       "      <td>0.025128</td>\n",
       "      <td>0.000299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>975</th>\n",
       "      <td>0.996225</td>\n",
       "      <td>0.056100</td>\n",
       "      <td>0.025113</td>\n",
       "      <td>0.000300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>976</th>\n",
       "      <td>0.996565</td>\n",
       "      <td>0.056432</td>\n",
       "      <td>0.025092</td>\n",
       "      <td>0.000304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>977</th>\n",
       "      <td>0.996109</td>\n",
       "      <td>0.057260</td>\n",
       "      <td>0.025078</td>\n",
       "      <td>0.000306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>978</th>\n",
       "      <td>0.995832</td>\n",
       "      <td>0.056726</td>\n",
       "      <td>0.025068</td>\n",
       "      <td>0.000307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>979</th>\n",
       "      <td>0.996289</td>\n",
       "      <td>0.056154</td>\n",
       "      <td>0.025056</td>\n",
       "      <td>0.000310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>980</th>\n",
       "      <td>0.996765</td>\n",
       "      <td>0.056522</td>\n",
       "      <td>0.025046</td>\n",
       "      <td>0.000311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>981</th>\n",
       "      <td>0.996554</td>\n",
       "      <td>0.056492</td>\n",
       "      <td>0.025029</td>\n",
       "      <td>0.000311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>982</th>\n",
       "      <td>0.996248</td>\n",
       "      <td>0.056869</td>\n",
       "      <td>0.025009</td>\n",
       "      <td>0.000311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>983</th>\n",
       "      <td>0.995642</td>\n",
       "      <td>0.056887</td>\n",
       "      <td>0.024978</td>\n",
       "      <td>0.000314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>984</th>\n",
       "      <td>0.996137</td>\n",
       "      <td>0.057002</td>\n",
       "      <td>0.024961</td>\n",
       "      <td>0.000321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>985</th>\n",
       "      <td>0.996417</td>\n",
       "      <td>0.057318</td>\n",
       "      <td>0.024935</td>\n",
       "      <td>0.000317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>986</th>\n",
       "      <td>0.997005</td>\n",
       "      <td>0.057073</td>\n",
       "      <td>0.024915</td>\n",
       "      <td>0.000319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>987</th>\n",
       "      <td>0.996979</td>\n",
       "      <td>0.056948</td>\n",
       "      <td>0.024891</td>\n",
       "      <td>0.000319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>988</th>\n",
       "      <td>0.997593</td>\n",
       "      <td>0.057869</td>\n",
       "      <td>0.024862</td>\n",
       "      <td>0.000314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>989</th>\n",
       "      <td>0.998409</td>\n",
       "      <td>0.057031</td>\n",
       "      <td>0.024842</td>\n",
       "      <td>0.000329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>990</th>\n",
       "      <td>0.998490</td>\n",
       "      <td>0.056996</td>\n",
       "      <td>0.024823</td>\n",
       "      <td>0.000332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>991</th>\n",
       "      <td>0.997861</td>\n",
       "      <td>0.056947</td>\n",
       "      <td>0.024803</td>\n",
       "      <td>0.000330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>992</th>\n",
       "      <td>0.997713</td>\n",
       "      <td>0.056959</td>\n",
       "      <td>0.024796</td>\n",
       "      <td>0.000332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>993</th>\n",
       "      <td>0.997546</td>\n",
       "      <td>0.056581</td>\n",
       "      <td>0.024773</td>\n",
       "      <td>0.000342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>994</th>\n",
       "      <td>0.997174</td>\n",
       "      <td>0.056622</td>\n",
       "      <td>0.024753</td>\n",
       "      <td>0.000338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>0.997373</td>\n",
       "      <td>0.056958</td>\n",
       "      <td>0.024728</td>\n",
       "      <td>0.000342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>0.997284</td>\n",
       "      <td>0.056981</td>\n",
       "      <td>0.024697</td>\n",
       "      <td>0.000336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>0.996666</td>\n",
       "      <td>0.057738</td>\n",
       "      <td>0.024684</td>\n",
       "      <td>0.000325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>0.997161</td>\n",
       "      <td>0.058025</td>\n",
       "      <td>0.024669</td>\n",
       "      <td>0.000331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>0.997438</td>\n",
       "      <td>0.058298</td>\n",
       "      <td>0.024657</td>\n",
       "      <td>0.000326</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     test-mlogloss-mean  test-mlogloss-std  train-mlogloss-mean  \\\n",
       "0              1.047559           0.005322             1.040275   \n",
       "1              1.010883           0.003960             0.988056   \n",
       "2              0.974102           0.004738             0.942376   \n",
       "3              0.943336           0.004458             0.900392   \n",
       "4              0.913940           0.004073             0.862032   \n",
       "5              0.888710           0.004376             0.829401   \n",
       "6              0.868966           0.005224             0.798906   \n",
       "7              0.850290           0.004816             0.772262   \n",
       "8              0.833376           0.003953             0.748114   \n",
       "9              0.818894           0.004849             0.726744   \n",
       "10             0.807209           0.007262             0.706485   \n",
       "11             0.796560           0.007789             0.688447   \n",
       "12             0.785693           0.008242             0.670807   \n",
       "13             0.777823           0.005168             0.652904   \n",
       "14             0.770674           0.005357             0.638080   \n",
       "15             0.762001           0.007438             0.624562   \n",
       "16             0.756956           0.006003             0.611841   \n",
       "17             0.750813           0.007029             0.598423   \n",
       "18             0.747629           0.006239             0.587672   \n",
       "19             0.743270           0.006364             0.577078   \n",
       "20             0.738120           0.007295             0.566394   \n",
       "21             0.733781           0.007588             0.556401   \n",
       "22             0.728309           0.008741             0.547351   \n",
       "23             0.725192           0.008494             0.538262   \n",
       "24             0.722602           0.006831             0.529843   \n",
       "25             0.720975           0.008532             0.521189   \n",
       "26             0.720737           0.008103             0.512666   \n",
       "27             0.719949           0.009091             0.504758   \n",
       "28             0.718626           0.008715             0.497604   \n",
       "29             0.715163           0.009193             0.490549   \n",
       "..                  ...                ...                  ...   \n",
       "970            0.996984           0.056560             0.025210   \n",
       "971            0.997045           0.056838             0.025184   \n",
       "972            0.997066           0.057347             0.025169   \n",
       "973            0.996820           0.057071             0.025152   \n",
       "974            0.996122           0.056560             0.025128   \n",
       "975            0.996225           0.056100             0.025113   \n",
       "976            0.996565           0.056432             0.025092   \n",
       "977            0.996109           0.057260             0.025078   \n",
       "978            0.995832           0.056726             0.025068   \n",
       "979            0.996289           0.056154             0.025056   \n",
       "980            0.996765           0.056522             0.025046   \n",
       "981            0.996554           0.056492             0.025029   \n",
       "982            0.996248           0.056869             0.025009   \n",
       "983            0.995642           0.056887             0.024978   \n",
       "984            0.996137           0.057002             0.024961   \n",
       "985            0.996417           0.057318             0.024935   \n",
       "986            0.997005           0.057073             0.024915   \n",
       "987            0.996979           0.056948             0.024891   \n",
       "988            0.997593           0.057869             0.024862   \n",
       "989            0.998409           0.057031             0.024842   \n",
       "990            0.998490           0.056996             0.024823   \n",
       "991            0.997861           0.056947             0.024803   \n",
       "992            0.997713           0.056959             0.024796   \n",
       "993            0.997546           0.056581             0.024773   \n",
       "994            0.997174           0.056622             0.024753   \n",
       "995            0.997373           0.056958             0.024728   \n",
       "996            0.997284           0.056981             0.024697   \n",
       "997            0.996666           0.057738             0.024684   \n",
       "998            0.997161           0.058025             0.024669   \n",
       "999            0.997438           0.058298             0.024657   \n",
       "\n",
       "     train-mlogloss-std  \n",
       "0              0.002585  \n",
       "1              0.004265  \n",
       "2              0.004495  \n",
       "3              0.006483  \n",
       "4              0.005640  \n",
       "5              0.003905  \n",
       "6              0.003260  \n",
       "7              0.002969  \n",
       "8              0.004163  \n",
       "9              0.004047  \n",
       "10             0.005171  \n",
       "11             0.004824  \n",
       "12             0.005061  \n",
       "13             0.005319  \n",
       "14             0.004880  \n",
       "15             0.004929  \n",
       "16             0.005025  \n",
       "17             0.005793  \n",
       "18             0.005346  \n",
       "19             0.005047  \n",
       "20             0.004576  \n",
       "21             0.004546  \n",
       "22             0.004496  \n",
       "23             0.003985  \n",
       "24             0.003710  \n",
       "25             0.003521  \n",
       "26             0.003652  \n",
       "27             0.004209  \n",
       "28             0.004119  \n",
       "29             0.003704  \n",
       "..                  ...  \n",
       "970            0.000298  \n",
       "971            0.000293  \n",
       "972            0.000297  \n",
       "973            0.000294  \n",
       "974            0.000299  \n",
       "975            0.000300  \n",
       "976            0.000304  \n",
       "977            0.000306  \n",
       "978            0.000307  \n",
       "979            0.000310  \n",
       "980            0.000311  \n",
       "981            0.000311  \n",
       "982            0.000311  \n",
       "983            0.000314  \n",
       "984            0.000321  \n",
       "985            0.000317  \n",
       "986            0.000319  \n",
       "987            0.000319  \n",
       "988            0.000314  \n",
       "989            0.000329  \n",
       "990            0.000332  \n",
       "991            0.000330  \n",
       "992            0.000332  \n",
       "993            0.000342  \n",
       "994            0.000338  \n",
       "995            0.000342  \n",
       "996            0.000336  \n",
       "997            0.000325  \n",
       "998            0.000331  \n",
       "999            0.000326  \n",
       "\n",
       "[1000 rows x 4 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 定义变量\n",
    "xgb_c = XGBClassifier(learning_rate = 0.1, \n",
    "                     n_estimators = 1000, \n",
    "                     max_depth = 5, \n",
    "                     min_child_weight = 1, \n",
    "                     gamma = 0, \n",
    "                     subsample = 0.3, \n",
    "                     colsample_bytree = 0.8, \n",
    "                     colsample_bylevel = 0.7,\n",
    "                     objective = 'multi:softprob', #The result contains predicted probability of each data point belonging to each class.）\n",
    "                     seed = 3)\n",
    "\n",
    "param = xgb_c.get_xgb_params()\n",
    "param['num_class'] = 3\n",
    "\n",
    "xgb_cv = xgb.cv(params = param, \n",
    "                dtrain = xgb.DMatrix(X_train, label = y_train), \n",
    "                num_boost_round = xgb_c.get_params()['n_estimators'], nfold=5,\n",
    "                #early_stopping_rounds = 50, #也可以用来确定n_eatimator\n",
    "                metrics='mlogloss')\n",
    "\n",
    "xgb_cv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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mTpzIZZddxpAhQwA477zzGDVqFIMGDeK5556rfV6PHj3YuXMn69at45hjjuH6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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1014697f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 获得xgb_cv的数据\n",
    "test_means = xgb_cv['test-mlogloss-mean']\n",
    "train_means = xgb_cv['train-mlogloss-mean']\n",
    "\n",
    "# 画图观察误差和n_estimators的关系\n",
    "#plt.figure(figsize=(15, 5))\n",
    "plt.plot(range(0, len(train_means)), train_means)\n",
    "plt.plot(range(0, len(test_means)), test_means)\n",
    "plt.xticks(range(0, 1100, 100))\n",
    "plt.xlim(0, 1000)\n",
    "plt.ylim(0, 1) \n",
    "plt.title('n_estimators & logloss')\n",
    "plt.xlabel('n_estimators')\n",
    "plt.ylabel('logloss')\n",
    "plt.grid(True)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "由图可以看出最佳n_estimator在0～50之间"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "best n_estimator:  34\n"
     ]
    }
   ],
   "source": [
    "# 通过cv结果获得测试集上误差最小时的n_estimator\n",
    "test_logloss_sort = pd.Series(xgb_cv['test-mlogloss-mean']).sort_values()\n",
    "best_estimator = list(test_logloss_sort.keys())[0]\n",
    "print ('best n_estimator: ', best_estimator)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 对树的最大深度 max_depth 和 min_child_weight 进行调优"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GridSearchCV_xgbClassifier 训练集最佳评价结果： 0.70625\n",
      "最佳正则参数和正则函数： {'max_depth': 7, 'min_child_weight': 1}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/hankaei/anaconda3/lib/python3.6/site-packages/sklearn/model_selection/_search.py:747: DeprecationWarning: The grid_scores_ attribute was deprecated in version 0.18 in favor of the more elaborate cv_results_ attribute. The grid_scores_ attribute will not be available from 0.20\n",
      "  DeprecationWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[mean: 0.68625, std: 0.03150, params: {'max_depth': 3, 'min_child_weight': 1},\n",
       " mean: 0.68875, std: 0.02027, params: {'max_depth': 3, 'min_child_weight': 2},\n",
       " mean: 0.68375, std: 0.02861, params: {'max_depth': 3, 'min_child_weight': 3},\n",
       " mean: 0.69875, std: 0.02083, params: {'max_depth': 3, 'min_child_weight': 4},\n",
       " mean: 0.68625, std: 0.02642, params: {'max_depth': 3, 'min_child_weight': 5},\n",
       " mean: 0.69750, std: 0.01854, params: {'max_depth': 4, 'min_child_weight': 1},\n",
       " mean: 0.69000, std: 0.01546, params: {'max_depth': 4, 'min_child_weight': 2},\n",
       " mean: 0.69375, std: 0.02551, params: {'max_depth': 4, 'min_child_weight': 3},\n",
       " mean: 0.69250, std: 0.02410, params: {'max_depth': 4, 'min_child_weight': 4},\n",
       " mean: 0.68625, std: 0.03658, params: {'max_depth': 4, 'min_child_weight': 5},\n",
       " mean: 0.67750, std: 0.02541, params: {'max_depth': 5, 'min_child_weight': 1},\n",
       " mean: 0.69000, std: 0.02091, params: {'max_depth': 5, 'min_child_weight': 2},\n",
       " mean: 0.69625, std: 0.02803, params: {'max_depth': 5, 'min_child_weight': 3},\n",
       " mean: 0.68250, std: 0.03811, params: {'max_depth': 5, 'min_child_weight': 4},\n",
       " mean: 0.67875, std: 0.03261, params: {'max_depth': 5, 'min_child_weight': 5},\n",
       " mean: 0.69500, std: 0.02007, params: {'max_depth': 6, 'min_child_weight': 1},\n",
       " mean: 0.69375, std: 0.02932, params: {'max_depth': 6, 'min_child_weight': 2},\n",
       " mean: 0.69125, std: 0.02758, params: {'max_depth': 6, 'min_child_weight': 3},\n",
       " mean: 0.69000, std: 0.03221, params: {'max_depth': 6, 'min_child_weight': 4},\n",
       " mean: 0.68500, std: 0.03036, params: {'max_depth': 6, 'min_child_weight': 5},\n",
       " mean: 0.70625, std: 0.02885, params: {'max_depth': 7, 'min_child_weight': 1},\n",
       " mean: 0.69375, std: 0.03726, params: {'max_depth': 7, 'min_child_weight': 2},\n",
       " mean: 0.68500, std: 0.02983, params: {'max_depth': 7, 'min_child_weight': 3},\n",
       " mean: 0.68625, std: 0.03234, params: {'max_depth': 7, 'min_child_weight': 4},\n",
       " mean: 0.68000, std: 0.03701, params: {'max_depth': 7, 'min_child_weight': 5},\n",
       " mean: 0.68625, std: 0.02049, params: {'max_depth': 8, 'min_child_weight': 1},\n",
       " mean: 0.69125, std: 0.02945, params: {'max_depth': 8, 'min_child_weight': 2},\n",
       " mean: 0.68000, std: 0.02853, params: {'max_depth': 8, 'min_child_weight': 3},\n",
       " mean: 0.68125, std: 0.03188, params: {'max_depth': 8, 'min_child_weight': 4},\n",
       " mean: 0.68125, std: 0.03306, params: {'max_depth': 8, 'min_child_weight': 5},\n",
       " mean: 0.68125, std: 0.01728, params: {'max_depth': 9, 'min_child_weight': 1},\n",
       " mean: 0.68875, std: 0.02724, params: {'max_depth': 9, 'min_child_weight': 2},\n",
       " mean: 0.68875, std: 0.03100, params: {'max_depth': 9, 'min_child_weight': 3},\n",
       " mean: 0.68750, std: 0.03306, params: {'max_depth': 9, 'min_child_weight': 4},\n",
       " mean: 0.67625, std: 0.01435, params: {'max_depth': 9, 'min_child_weight': 5}]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 代入最佳n_estimator，并调用 GridSearchCV 寻找最大深度和min_child_weight\n",
    "max_depth = range(3, 10, 1)\n",
    "min_child_weight = range(1, 6, 1)\n",
    "xgb_c.set_params(n_estimators = 34)  \n",
    "\n",
    "grid = GridSearchCV(xgb_c, dict(max_depth = max_depth, min_child_weight = min_child_weight), cv = 5)\n",
    "grid.fit(X_train, y_train)\n",
    "\n",
    "print('GridSearchCV_xgbClassifier 训练集最佳评价结果：', grid.best_score_)\n",
    "print('最佳正则参数和正则函数：', grid.best_params_)\n",
    "grid.grid_scores_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'mean_fit_time': array([ 0.18484626,  0.18122859,  0.19020958,  0.18456464,  0.18737841,\n",
       "         0.2299098 ,  0.22587051,  0.2805172 ,  0.25250983,  0.21881466,\n",
       "         0.25851321,  0.26930146,  0.25532651,  0.2825942 ,  0.26833558,\n",
       "         0.3300643 ,  0.2971252 ,  0.29354615,  0.27633557,  0.25250425,\n",
       "         0.32550969,  0.30743332,  0.29818473,  0.27731915,  0.2579196 ,\n",
       "         0.34726205,  0.31779132,  0.30516567,  0.27418337,  0.26413813,\n",
       "         0.35511065,  0.32732244,  0.31099763,  0.28549719,  0.27280726]),\n",
       " 'mean_score_time': array([ 0.00273094,  0.00249619,  0.00252318,  0.00248971,  0.00251894,\n",
       "         0.00262189,  0.00322967,  0.00379267,  0.00287733,  0.00286222,\n",
       "         0.00277019,  0.00277209,  0.00268655,  0.00312915,  0.00310459,\n",
       "         0.00324569,  0.00282941,  0.00294681,  0.00281973,  0.00279655,\n",
       "         0.0029285 ,  0.00286446,  0.00311241,  0.00287104,  0.00271583,\n",
       "         0.00293322,  0.00291576,  0.00283327,  0.00272884,  0.00269237,\n",
       "         0.0030129 ,  0.00287838,  0.00281005,  0.0036202 ,  0.00264559]),\n",
       " 'mean_test_score': array([ 0.68625,  0.68875,  0.68375,  0.69875,  0.68625,  0.6975 ,\n",
       "         0.69   ,  0.69375,  0.6925 ,  0.68625,  0.6775 ,  0.69   ,\n",
       "         0.69625,  0.6825 ,  0.67875,  0.695  ,  0.69375,  0.69125,\n",
       "         0.69   ,  0.685  ,  0.70625,  0.69375,  0.685  ,  0.68625,\n",
       "         0.68   ,  0.68625,  0.69125,  0.68   ,  0.68125,  0.68125,\n",
       "         0.68125,  0.68875,  0.68875,  0.6875 ,  0.67625]),\n",
       " 'mean_train_score': array([ 0.77187672,  0.77124977,  0.7653108 ,  0.75656128,  0.75436939,\n",
       "         0.80687236,  0.79780986,  0.78374979,  0.77062184,  0.76562037,\n",
       "         0.83750081,  0.81842999,  0.7968748 ,  0.77781375,  0.77531032,\n",
       "         0.85812437,  0.8287508 ,  0.81656182,  0.79875176,  0.77437477,\n",
       "         0.87281091,  0.84124691,  0.8262513 ,  0.78968876,  0.7790613 ,\n",
       "         0.8731195 ,  0.83781186,  0.81468975,  0.7893787 ,  0.77281081,\n",
       "         0.87781238,  0.83656624,  0.81531085,  0.79781473,  0.77718679]),\n",
       " 'param_max_depth': masked_array(data = [3 3 3 3 3 4 4 4 4 4 5 5 5 5 5 6 6 6 6 6 7 7 7 7 7 8 8 8 8 8 9 9 9 9 9],\n",
       "              mask = [False False False False False False False False False False False False\n",
       "  False False False False False False False False False False False False\n",
       "  False False False False False False False False False False False],\n",
       "        fill_value = ?),\n",
       " 'param_min_child_weight': masked_array(data = [1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5],\n",
       "              mask = [False False False False False False False False False False False False\n",
       "  False False False False False False False False False False False False\n",
       "  False False False False False False False False False False False],\n",
       "        fill_value = ?),\n",
       " 'params': [{'max_depth': 3, 'min_child_weight': 1},\n",
       "  {'max_depth': 3, 'min_child_weight': 2},\n",
       "  {'max_depth': 3, 'min_child_weight': 3},\n",
       "  {'max_depth': 3, 'min_child_weight': 4},\n",
       "  {'max_depth': 3, 'min_child_weight': 5},\n",
       "  {'max_depth': 4, 'min_child_weight': 1},\n",
       "  {'max_depth': 4, 'min_child_weight': 2},\n",
       "  {'max_depth': 4, 'min_child_weight': 3},\n",
       "  {'max_depth': 4, 'min_child_weight': 4},\n",
       "  {'max_depth': 4, 'min_child_weight': 5},\n",
       "  {'max_depth': 5, 'min_child_weight': 1},\n",
       "  {'max_depth': 5, 'min_child_weight': 2},\n",
       "  {'max_depth': 5, 'min_child_weight': 3},\n",
       "  {'max_depth': 5, 'min_child_weight': 4},\n",
       "  {'max_depth': 5, 'min_child_weight': 5},\n",
       "  {'max_depth': 6, 'min_child_weight': 1},\n",
       "  {'max_depth': 6, 'min_child_weight': 2},\n",
       "  {'max_depth': 6, 'min_child_weight': 3},\n",
       "  {'max_depth': 6, 'min_child_weight': 4},\n",
       "  {'max_depth': 6, 'min_child_weight': 5},\n",
       "  {'max_depth': 7, 'min_child_weight': 1},\n",
       "  {'max_depth': 7, 'min_child_weight': 2},\n",
       "  {'max_depth': 7, 'min_child_weight': 3},\n",
       "  {'max_depth': 7, 'min_child_weight': 4},\n",
       "  {'max_depth': 7, 'min_child_weight': 5},\n",
       "  {'max_depth': 8, 'min_child_weight': 1},\n",
       "  {'max_depth': 8, 'min_child_weight': 2},\n",
       "  {'max_depth': 8, 'min_child_weight': 3},\n",
       "  {'max_depth': 8, 'min_child_weight': 4},\n",
       "  {'max_depth': 8, 'min_child_weight': 5},\n",
       "  {'max_depth': 9, 'min_child_weight': 1},\n",
       "  {'max_depth': 9, 'min_child_weight': 2},\n",
       "  {'max_depth': 9, 'min_child_weight': 3},\n",
       "  {'max_depth': 9, 'min_child_weight': 4},\n",
       "  {'max_depth': 9, 'min_child_weight': 5}],\n",
       " 'rank_test_score': array([19, 15, 26,  2, 19,  3, 12,  6,  9, 19, 34, 12,  4, 27, 33,  5,  6,\n",
       "        10, 12, 24,  1,  6, 24, 19, 31, 19, 10, 31, 28, 28, 28, 15, 15, 18,\n",
       "        35], dtype=int32),\n",
       " 'split0_test_score': array([ 0.69565217,  0.68322981,  0.72049689,  0.71428571,  0.70807453,\n",
       "         0.70186335,  0.70186335,  0.70807453,  0.68944099,  0.71428571,\n",
       "         0.69565217,  0.70186335,  0.73291925,  0.73291925,  0.67701863,\n",
       "         0.70186335,  0.73291925,  0.70807453,  0.69565217,  0.69565217,\n",
       "         0.72049689,  0.72049689,  0.71428571,  0.71428571,  0.70807453,\n",
       "         0.68944099,  0.71428571,  0.69565217,  0.72670807,  0.70807453,\n",
       "         0.68322981,  0.69565217,  0.72670807,  0.70807453,  0.67701863]),\n",
       " 'split0_train_score': array([ 0.76682316,  0.76838811,  0.76056338,  0.75117371,  0.74647887,\n",
       "         0.79655712,  0.78873239,  0.78090767,  0.75899844,  0.75117371,\n",
       "         0.83568075,  0.80125196,  0.79029734,  0.77934272,  0.76212833,\n",
       "         0.85602504,  0.82159624,  0.80751174,  0.80125196,  0.76525822,\n",
       "         0.86541471,  0.83411581,  0.82942097,  0.79342723,  0.77308294,\n",
       "         0.85602504,  0.83881064,  0.81220657,  0.79029734,  0.771518  ,\n",
       "         0.87480438,  0.83255086,  0.81064163,  0.79186228,  0.77308294]),\n",
       " 'split1_test_score': array([ 0.65625,  0.6625 ,  0.65   ,  0.68125,  0.6625 ,  0.68125,\n",
       "         0.68125,  0.675  ,  0.66875,  0.6375 ,  0.65625,  0.68125,\n",
       "         0.6875 ,  0.65625,  0.675  ,  0.69375,  0.7    ,  0.66875,\n",
       "         0.68125,  0.6875 ,  0.68125,  0.675  ,  0.6625 ,  0.6625 ,\n",
       "         0.675  ,  0.69375,  0.6625 ,  0.65   ,  0.65   ,  0.66875,\n",
       "         0.6625 ,  0.7    ,  0.65625,  0.6625 ,  0.66875]),\n",
       " 'split1_train_score': array([ 0.778125 ,  0.771875 ,  0.7765625,  0.7625   ,  0.75     ,\n",
       "         0.81875  ,  0.8046875,  0.7953125,  0.771875 ,  0.7671875,\n",
       "         0.8390625,  0.8203125,  0.8125   ,  0.778125 ,  0.775    ,\n",
       "         0.8625   ,  0.840625 ,  0.821875 ,  0.790625 ,  0.7875   ,\n",
       "         0.875    ,  0.84375  ,  0.8296875,  0.7921875,  0.7875   ,\n",
       "         0.8859375,  0.840625 ,  0.825    ,  0.790625 ,  0.78125  ,\n",
       "         0.8828125,  0.8453125,  0.81875  ,  0.8015625,  0.78125  ]),\n",
       " 'split2_test_score': array([ 0.7375 ,  0.71875,  0.7125 ,  0.725  ,  0.725  ,  0.73125,\n",
       "         0.7125 ,  0.7375 ,  0.7375 ,  0.7375 ,  0.71875,  0.71875,\n",
       "         0.725  ,  0.71875,  0.7375 ,  0.725  ,  0.70625,  0.725  ,\n",
       "         0.7375 ,  0.725  ,  0.75625,  0.75   ,  0.70625,  0.73125,\n",
       "         0.73125,  0.71875,  0.7375 ,  0.725  ,  0.7125 ,  0.71875,\n",
       "         0.7125 ,  0.71875,  0.725  ,  0.7375 ,  0.7    ]),\n",
       " 'split2_train_score': array([ 0.778125 ,  0.7640625,  0.74375  ,  0.740625 ,  0.7484375,\n",
       "         0.7953125,  0.7890625,  0.771875 ,  0.7703125,  0.76875  ,\n",
       "         0.840625 ,  0.81875  ,  0.7796875,  0.7703125,  0.778125 ,\n",
       "         0.8546875,  0.81875  ,  0.825    ,  0.8015625,  0.7640625,\n",
       "         0.871875 ,  0.8375   ,  0.815625 ,  0.7796875,  0.7765625,\n",
       "         0.865625 ,  0.825    ,  0.8265625,  0.7859375,  0.7640625,\n",
       "         0.8796875,  0.84375  ,  0.8109375,  0.79375  ,  0.771875 ]),\n",
       " 'split3_test_score': array([ 0.65   ,  0.675  ,  0.65625,  0.66875,  0.65625,  0.68125,\n",
       "         0.66875,  0.675  ,  0.675  ,  0.65625,  0.65625,  0.65625,\n",
       "         0.6625 ,  0.63125,  0.6375 ,  0.6625 ,  0.64375,  0.65   ,\n",
       "         0.6375 ,  0.63125,  0.68125,  0.64375,  0.6375 ,  0.64375,\n",
       "         0.625  ,  0.66875,  0.66875,  0.65   ,  0.65625,  0.625  ,\n",
       "         0.66875,  0.6375 ,  0.675  ,  0.64375,  0.65625]),\n",
       " 'split3_train_score': array([ 0.775    ,  0.7828125,  0.7796875,  0.7734375,  0.7625   ,\n",
       "         0.81875  ,  0.809375 ,  0.7890625,  0.7828125,  0.775    ,\n",
       "         0.8390625,  0.8265625,  0.8109375,  0.7859375,  0.7921875,\n",
       "         0.859375 ,  0.84375  ,  0.81875  ,  0.8046875,  0.7890625,\n",
       "         0.88125  ,  0.846875 ,  0.83125  ,  0.79375  ,  0.78125  ,\n",
       "         0.884375 ,  0.84375  ,  0.8046875,  0.8015625,  0.7703125,\n",
       "         0.8765625,  0.840625 ,  0.8203125,  0.8171875,  0.784375 ]),\n",
       " 'split4_test_score': array([ 0.6918239 ,  0.70440252,  0.67924528,  0.70440252,  0.67924528,\n",
       "         0.6918239 ,  0.68553459,  0.67295597,  0.6918239 ,  0.68553459,\n",
       "         0.66037736,  0.6918239 ,  0.67295597,  0.67295597,  0.66666667,\n",
       "         0.6918239 ,  0.68553459,  0.70440252,  0.69811321,  0.68553459,\n",
       "         0.6918239 ,  0.67924528,  0.70440252,  0.67924528,  0.66037736,\n",
       "         0.66037736,  0.67295597,  0.67924528,  0.66037736,  0.68553459,\n",
       "         0.67924528,  0.6918239 ,  0.66037736,  0.68553459,  0.67924528]),\n",
       " 'split4_train_score': array([ 0.76131045,  0.76911076,  0.76599064,  0.7550702 ,  0.76443058,\n",
       "         0.8049922 ,  0.79719189,  0.78159126,  0.76911076,  0.76599064,\n",
       "         0.83307332,  0.82527301,  0.79095164,  0.77535101,  0.76911076,\n",
       "         0.85803432,  0.81903276,  0.80967239,  0.79563183,  0.76599064,\n",
       "         0.87051482,  0.84399376,  0.82527301,  0.78939158,  0.77691108,\n",
       "         0.87363495,  0.84087363,  0.8049922 ,  0.77847114,  0.77691108,\n",
       "         0.87519501,  0.82059282,  0.81591264,  0.78471139,  0.77535101]),\n",
       " 'std_fit_time': array([ 0.00439585,  0.00163335,  0.0068562 ,  0.00436037,  0.00565796,\n",
       "         0.00735751,  0.00484591,  0.01341959,  0.02117664,  0.00694383,\n",
       "         0.00721564,  0.00607881,  0.00593037,  0.00416708,  0.01309112,\n",
       "         0.01474722,  0.00710665,  0.02052258,  0.00843182,  0.0038752 ,\n",
       "         0.0122083 ,  0.00782296,  0.00698472,  0.00674108,  0.00464331,\n",
       "         0.01519868,  0.00795807,  0.0108078 ,  0.00335879,  0.00446073,\n",
       "         0.01035426,  0.00631093,  0.00689313,  0.01236368,  0.00838082]),\n",
       " 'std_score_time': array([  2.25721918e-04,   1.80980904e-05,   6.61007072e-05,\n",
       "          5.92622459e-06,   9.34398152e-05,   9.67483675e-05,\n",
       "          1.24323080e-03,   6.17083229e-04,   6.01873800e-04,\n",
       "          5.60891491e-04,   3.99915453e-05,   5.69208567e-05,\n",
       "          3.95933947e-05,   7.52232049e-04,   5.82046206e-04,\n",
       "          3.58443272e-04,   2.54394300e-05,   3.73982274e-04,\n",
       "          9.49393918e-05,   1.06508878e-04,   1.90585793e-05,\n",
       "          1.71134724e-05,   6.13060720e-04,   3.53138787e-04,\n",
       "          8.45876057e-05,   4.90999664e-05,   7.28584084e-05,\n",
       "          7.42598824e-05,   1.40780872e-05,   2.19120035e-05,\n",
       "          1.02771296e-04,   3.27876781e-05,   9.96395713e-06,\n",
       "          1.86641722e-03,   3.69259814e-05]),\n",
       " 'std_test_score': array([ 0.03149747,  0.0202596 ,  0.02863849,  0.02083233,  0.02643358,\n",
       "         0.0185384 ,  0.01546486,  0.02550824,  0.0241047 ,  0.03659111,\n",
       "         0.02540709,  0.02091465,  0.02805155,  0.03815284,  0.0326046 ,\n",
       "         0.02007604,  0.0293469 ,  0.02758121,  0.03220752,  0.03036371,\n",
       "         0.02884806,  0.03726812,  0.02983683,  0.03235734,  0.03701975,\n",
       "         0.02046561,  0.02945646,  0.028538  ,  0.03190743,  0.03307222,\n",
       "         0.01727607,  0.02724134,  0.03101345,  0.03307222,  0.01435146]),\n",
       " 'std_train_score': array([ 0.00670852,  0.00630094,  0.01281529,  0.01100263,  0.0075349 ,\n",
       "         0.01025404,  0.00825049,  0.00794567,  0.00758505,  0.0078622 ,\n",
       "         0.00274057,  0.00907409,  0.01277229,  0.00511398,  0.01005045,\n",
       "         0.002718  ,  0.01105999,  0.00683504,  0.00500325,  0.011382  ,\n",
       "         0.00523187,  0.00469847,  0.00566951,  0.0052312 ,  0.00495198,\n",
       "         0.01131054,  0.00659835,  0.00946062,  0.00750545,  0.00587329,\n",
       "         0.00303243,  0.00911976,  0.00395308,  0.0110725 ,  0.00482922])}"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid.cv_results_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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X0EEHI0aMYPDgwTRv3pzzzjuPCRMmcNFFFzF06FCSkpKqXMdvvvkm9957L4MG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\nqjuR0TDkOtjyjbYsBCOleXC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J51Uf/OFSPZiurdItO/luzsZwxj06wbU/I8vzM3WZq+hW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\n9bQASesD1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      "text/plain": [
       "<matplotlib.figure.Figure at 0x101f19eb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 画图观察准确率和 max_depth 及 min_child_weight 的关系\n",
    "mean_test_scores = grid.cv_results_['mean_test_score']\n",
    "mean_train_scores = grid.cv_results_['mean_train_score']\n",
    "\n",
    "# name of xticks\n",
    "param_xticks = []\n",
    "for param in grid.cv_results_['params']:\n",
    "    param_xticks.append('depth' + str(param['max_depth']) + ', weight'+ str(param['min_child_weight']))\n",
    "#print(a)\n",
    "\n",
    "plt.figure(figsize = (10, 5))\n",
    "plt.plot(range(0, len(mean_test_scores)), mean_test_scores, label = 'mean_test_score')\n",
    "plt.plot(range(0, len(mean_train_scores)), mean_train_scores, label = 'mean_train_score')\n",
    "#plt.xticks(range(0, len(grid.cv_results_['params'])))\n",
    "plt.xticks(range(0, len(param_xticks)), param_xticks, rotation = 90)\n",
    "plt.title('scores of max_depth & min_child_weight')\n",
    "plt.xlabel('max_depth & min_child_weight')\n",
    "plt.ylabel('scores')\n",
    "plt.grid()\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GridSearchCV max_depth & min_child_weight 测试集评价结果： 0.675\n"
     ]
    }
   ],
   "source": [
    "print('GridSearchCV max_depth & min_child_weight 测试集评价结果：', grid.best_estimator_.score(X_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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UGlsk/SlhloNiZpr3LiISpXPnztSuXXu/NpXGFkkv+lua/iqb2RjgTGAd0JOg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WkX4HzgRNLuEH8Bfhu1udmanIo1zJKuAZZExM1Z+W3A3RFxf+4654olGVUFzLl69uwZy5Yta8ju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\nI34AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x101469048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 观察特征重要性\n",
    "#grid.best_estimator_.feature_importances_\n",
    "fig, ax = plt.subplots(figsize=(10, 15))\n",
    "xgb.plot_importance(grid.best_estimator_.fit(X_train, y_train), ax = ax)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 对正则参数进行调优"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GridSearchCV_L1 训练集最佳评价结果： 0.7025\n",
      "最佳正则参数： {'reg_alpha': 0.5, 'reg_lambda': 0.5}\n"
     ]
    }
   ],
   "source": [
    "# L1 : reg_alpha \n",
    "xgb_c.set_params(max_depth = 7, min_child_weight = 1)\n",
    "\n",
    "grid_reg1 = GridSearchCV(xgb_c, dict(reg_alpha = [0.1, 0.5, 1, 1.5, 2], reg_lambda = [0.1, 0.5, 1, 1.5, 2]), cv = 5)\n",
    "grid_reg1.fit(X_train, y_train)\n",
    "\n",
    "print('GridSearchCV_L1 训练集最佳评价结果：', grid_reg1.best_score_)\n",
    "print('最佳正则参数：', grid_reg1.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GridSearchCV_L2 训练集最佳评价结果： 0.7025\n",
      "最佳正则参数： {'reg_alpha': 0.5, 'reg_lambda': 0.5}\n"
     ]
    }
   ],
   "source": [
    "# L2 : reg_lambda\n",
    "grid_reg2 = GridSearchCV(xgb_c, dict(reg_alpha = [0.4, 0.45, 0.5, 0.65], reg_lambda = [0.4, 0.45, 0.5, 0.65]), cv = 5)\n",
    "grid_reg2.fit(X_train, y_train)\n",
    "\n",
    "print('GridSearchCV_L2 训练集最佳评价结果：', grid_reg2.best_score_)\n",
    "print('最佳正则参数：', grid_reg2.best_params_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "更小的范围也是0.5, 0.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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KM3WauOq0Vr2glVe1VqdxpV3G6SZigURV80TkHmAG7tHeSaq6WkQmAMmqOh34DfAvEXkA\nV0V1m6qqiFwOTBCRPCAfuFtVD3hJ/4Ljj/9+jjW0G2MCasVA/PluKqog3/VmkLoYUpPdlDKLwtrx\npu1daSUQYJp3tqfHQmQvJBpjzlw5mbBzqQsuO5a44HJ4r9tWMwYSurvg0ioJWia5jinPoCqxKn+P\npDqxQGKMCYmqqxrbkQypS1yA2bX8+DDb9Zq70krLnl6A6R257m2qgerwHokxxpxaRKDx2W7q8iO3\nLu8Y7FnlSis7vCqxdZ+4bbUbwAXXuH3b9YOoanJLLch3T71t+C9c8WjEq+iqyVUbY0w1VTMaWvZw\nk3ujwHs8eRGs/dh1+7LsTfeocedr3cuUrXtXfhVYbg5s+crlaf3nru+3qGgX5BK6RfTUVrVljDHh\nyM2BlJmuC/0NM1zHlI3auBt4l+tdo32kgkpOBmyc6UpIG2fCsUMQXR/OHwQdr4b2A6F2/XInb20k\nPhZIjDGVIicT1n/mgsqmOW5cmviOkHi9CyplHY46mEN7Yd2nLnhs/goKcqFuM+9lzquh3eUV9ra/\nBRIfCyTGmEp3eB+s+dD1IbZtgVvXsick3uCqwOqfFXpaBzbD2k9c8Ni+CFD3kmXHq10bTatebujq\nCmaBxMcCiTGmSqVvh9Xvu5LK7pUgNaDtpV7nlCNOfhlSFXavcCWPtZ/A3tVu/VmJ0PEa6Dg8slVm\nHgskPhZIjDHVRtoGWDXNBZUDm92QxO0HujaV+mcdr7ZK3wYItLkYLrjaBY9KHgjMAomPBRJjTLWj\n6l6GXPWem7J2ufVR0XDOABc8zh8K9eKrLIv2HokxxlRnIscfKx44AX74FnLS4Zz+YT1pVRUskBhj\nTFWrEQXtLqvqXJRbjarOgDHGmFObBRJjjDFhsUBijDEmLBZIjDHGhMUCiTHGmLBYIDHGGBMWCyTG\nGGPCEtFAIiJDRGS9iKSIyMNBtrcRkTkislREVojIMG/9QBFZIiIrvZ9X+I6Z66W5zJuaRfIajDHG\nlCxiLySKSBTwAjAQSAUWi8h0VV3j2+33wDuq+pKIdAI+A9oC+4BrVHWniHQBZgAtfceNVVXr88QY\nY6qBSJZIegMpqrpZVY8BU4GRRfZRoIE33xDYCaCqS1V1p7d+NRAjIhXTwb4xxpgKFclA0hLY7ltO\n5cRSBcB44GYRScWVRu4Nks6PgKWqetS37jWvWutRkeD9KIvIXSKSLCLJaWlp5b4IY4wxJYtkIAl2\ngy/a1fAY4HVVbQUMAyaLSGGeRKQz8ATwc98xY1U1EbjMm34S7OSqOlFVk1Q1KT6+6nrPNMaY010k\nA0kq0Nq33Aqv6srnZ8A7AKq6AIgB4gBEpBXwAXCLqm4KHKCqO7yfWcBbuCo0Y4wxVSSSgWQx0F5E\n2olINDAamF5kn23AlQAicgEukKSJSCPgU+ARVZ0f2FlEaopIINDUAq4GVkXwGowxxpQiYoFEVfOA\ne3BPXK3FPZ21WkQmiMgIb7ffAHeKyHJgCnCbupG27gHOAx4t8phvbWCGiKwAlgE7gH9F6hqMMcaU\nzkZINMYYE1SoIyTam+3GGGPCYoHEGGNMWCyQGGOMCYsFEmOMMWGxQGKMMSYsFkiMMcaExQKJMcaY\nsFggMcYYExYLJMYYY8JigcQYY0xYLJAYY4wJiwUSY4wxYbFAYowxJiwWSIwxxoTFAokxxpiwWCAx\nxhgTFgskxhhjwmKBxBhjTFgiGkhEZIiIrBeRFBF5OMj2NiIyR0SWisgKERnm2/aId9x6ERkcaprG\nGGMqV8QCiYhEAS8AQ4FOwBgR6VRkt98D76jqhcBo4EXv2E7ecmdgCPCiiESFmKYxxphKFMkSSW8g\nRVU3q+oxYCowssg+CjTw5hsCO735kcBUVT2qqluAFC+9UNI0xhhTiSIZSFoC233Lqd46v/HAzSKS\nCnwG3FvKsaGkCYCI3CUiySKSnJaWVt5rMMYYU4pIBhIJsk6LLI8BXlfVVsAwYLKI1Cjh2FDSdCtV\nJ6pqkqomxcfHlyHbxhhjyqJmBNNOBVr7lltxvOoq4Ge4NhBUdYGIxABxpRxbWprGGGMqUSRLJIuB\n9iLSTkSicY3n04vssw24EkBELgBigDRvv9EiUltE2gHtgUUhpmmMMaYSRaxEoqp5InIPMAOIAiap\n6moRmQAkq+p04DfAv0TkAVwV1W2qqsBqEXkHWAPkAb9S1XyAYGlG6hqMMcaUTtx9+/SWlJSkycnJ\nVZ0NY4w5pYjIElVNKm0/e7PdGGNMWCyQGGOMCYsFEmOMMWGxQGKMMSYsFkiMMcaExQKJMcaYsFgg\nMcYYExYLJMYYY8ISyb62jDGnqNzcXFJTU8nJyanqrJhKEBMTQ6tWrahVq1a5jrdAYow5SWpqKvXr\n16dt27aIBOt025wuVJX9+/eTmppKu3btypWGVW0ZY06Sk5ND06ZNLYicAUSEpk2bhlX6tEBijAnK\ngsiZI9zfdUiBRERuEJH63vzvReR9EekR1pmNMaaae+aZZ8jOzi7XsR9++CFr1qyp4BxVT6GWSB5V\n1SwRuRQYDLwBvBS5bBljTNU71QJJfn5+pZ4vINRAEsjdcOAlVf0IiI5MlowxBrZu3UrHjh254447\n6NKlC2PHjmXWrFn07duX9u3bs2jRIg4fPsxPf/pTevXqxYUXXshHH31UeOxll11Gjx496NGjB99+\n+y0Ac+fOpX///lx//fV07NiRsWPHUtxQGv/4xz/YuXMnAwYMYMCAAQB88cUXXHzxxfTo0YMbbriB\nQ4cOAfDwww/TqVMnunbtyoMPPsi3337L9OnTeeihh+jevTubNm0q9hyB40aPHg3AoUOHuP3220lM\nTKRr16689957AEyZMoXExES6dOnCuHHjCtOoV68ejz32GBdddBELFixgyZIl9OvXj549ezJ48GB2\n7dpVAb+NkoU0HomIfALsAK4CegJHgEWq2i2y2asYNh6JMWWzdu1aLrjgAgD+8PFq1uzMrND0O7Vo\nwOPXdC5xn61bt3LeeeexdOlSOnfuTK9evejWrRuvvvoq06dP57XXXqNTp0506tSJm2++mfT0dHr3\n7s3SpUsREWrUqEFMTAwbN25kzJgxJCcnM3fuXEaOHMnq1atp0aIFffv25a9//SuXXnpp0Dy0bduW\n5ORk4uLi2LdvH9dddx2ff/45devW5YknnuDo0aPcc889XHzxxaxbtw4RIT09nUaNGnHbbbdx9dVX\nc/311xd7jS1atGDLli3Url278Lhx48Zx9OhRnnnmGQAOHjzIkSNH6NOnD0uWLKFx48YMGjSI++67\nj1GjRiEivP3229x4443k5ubSr18/PvroI+Lj43n77beZMWMGkyZNKvV34v+dB4Q6Hkmoj//eiBtb\n/SlVTReRBOChEI81xphyadeuHYmJiQB07tyZK6+8EhEhMTGRrVu3kpqayvTp03nqqacA97TZtm3b\naNGiBffccw/Lli0jKiqKDRs2FKbZu3dvWrVqBUD37t3ZunVrsYHEb+HChaxZs4a+ffsCcOzYMS6+\n+GIaNGhATEwMd9xxB8OHD+fqq68O+fq6du3K2LFjGTVqFKNGjQJg1qxZTJ06tXCfxo0bM2/ePPr3\n7098fDwAY8eOZd68eYwaNYqoqCh+9KMfAbB+/XpWrVrFwIEDAVfVlZCQEHJ+yiukQKKq2SKyF7gU\n2Igb/nZjJDNmjKkeSis5RFLt2rUL52vUqFG4XKNGDfLy8oiKiuK9996jQ4cOJxw3fvx4mjdvzvLl\nyykoKCAmJiZomlFRUeTl5YWUF1Vl4MCBTJky5aRtixYt4ssvv2Tq1Kk8//zzzJ49O6Q0P/30U+bN\nm8f06dP54x//yOrVq1HVk56iKqnmKCYmhqioqML9OnfuzIIFC0I6f0UJ9amtx4FxwCPeqlrAf0I4\nboiIrBeRFBF5OMj2p0VkmTdtEJF0b/0A3/plIpIjIqO8ba+LyBbftu6hXqwx5vQyePBgnnvuucIb\n7dKlSwHIyMggISGBGjVqMHny5HI3QtevX5+srCwA+vTpw/z580lJSQEgOzubDRs2cOjQITIyMhg2\nbBjPPPMMy5YtO+nYYAoKCti+fTsDBgzgySefJD09nUOHDjFo0CCef/75wv0OHjzIRRddxFdffcW+\nffvIz89nypQp9OvX76Q0O3ToQFpaWmEgyc3NZfXq1eW69rIItbH9WmAEcBhAVXcC9Us6QESigBeA\noUAnYIyIdPLvo6oPqGp3Ve0OPAe8762f41t/BZANfOE79KHAdlVdFuI1GGNOM48++ii5ubl07dqV\nLl268OijjwLwy1/+kjfeeIM+ffqwYcMG6tatW67077rrLoYOHcqAAQOIj4/n9ddfZ8yYMXTt2pU+\nffqwbt06srKyuPrqq+natSv9+vXj6aefBmD06NH89a9/5cILLwza2J6fn8/NN99MYmIiF154IQ88\n8ACNGjXi97//PQcPHqRLly5069aNOXPmkJCQwJ///GcGDBhAt27d6NGjByNHjjwpzejoaKZNm8a4\ncePo1q0b3bt3L3zQIJJCbWxfpKq9ReR7Ve0hInWBBaratYRjLgbGq+pgb/kRAFX9czH7fws8rqoz\ni6y/C+inqmO95deBT1R1WkhXiDW2G1NWwRpezektnMb2UEsk74jIP4FGInInMAv4VynHtAS2+5ZT\nvXUnEZGzgXZAsIrF0UDRSsk/icgKr2qsdpBjEJG7RCRZRJLT0tJKyaoxxpjyCrWx/SkRGQhkAh2A\nx4qWHIII9s59ccWf0cA0VT2hItN7OiwRmOFb/QiwG/cey0Rc282EIHme6G0nKSmp9GKXMeaMde21\n17Jly5YT1j3xxBMMHjy4QtL/1a9+xfz5809Yd//993P77bdXSPpVrdRA4rV1zFDVq4DSgodfKtDa\nt9wK2FnMvqOBXwVZfyPwgarmBlaoauDtmqMi8hrwYBnyZIwxJ/nggw8imv4LL7wQ0fSrWqlVW14p\nIVtEGpYx7cVAexFpJyLRuGAxvehOItIBaAwEe15tDEWqtbxSCuKejxsFrCpjvowxxlSgUF9IzAFW\nishMvCe3AFT1vuIOUNU8EbkHVy0VBUxS1dUiMgFIVtVAUBkDTNUirf4i0hZXovmqSNJvikg8rups\nGXB3iNdgjDEmAkINJJ96U5mo6mfAZ0XWPVZkeXwxx24lSOO8ql5R1nwYY4yJnFAb29/wqqfO91at\n97dbGGOMOXOFFEhEpD+u6/ituCql1iJyq6rOi1zWjDHGnApCfY/kb8AgVe2nqpfjxiR5OnLZMsaY\nqlfe8Ugee+wxZs2aFYEcVU+hBpJaqro+sKCqG3D9bRljzGmrpEBSUv9dEyZM4KqrropUtkIWaoeU\n4Qq1sT1ZRF4FJnvLY4ElkcmSMaZa+fxh2L2yYtM8KxGG/qXEXbZu3cqQIUO49NJLWbhwId26deP2\n22/n8ccfZ+/evbz55pt07tyZe++9l5UrV5KXl8f48eMZOXIkW7du5Sc/+QmHD7uHTJ9//nkuueQS\n5s6dy/jx44mLi2PVqlX07NmT//znP0HHLPcPbBUXF8ecOXOoV68ev/71r5kxYwZ/+9vfmD17Nh9/\n/DFHjhzhkksu4Z///CcicsJYJG3btuXWW2/l448/Jjc3l3fffZeOHTsGveavvvqK+++/H3DjqM+b\nN4/69evz5JNPMnnyZGrUqMHQoUP5y1/+wrJly7j77rvJzs7m3HPPZdKkSTRu3Jj+/ftzySWXMH/+\nfEaMGMEtt9zC3XffzbZt2wAXHANd4VeUUAPJL3AvDN6HayOZB7xYoTkxxpgiUlJSePfdd5k4cSK9\nevXirbfe4ptvvmH69On83//9H506deKKK65g0qRJhQNbXXXVVTRr1oyZM2eeNLAVuB6C/QNbzZ8/\nP+h4JPfddx9///vfmTNnDnFxcQAcPnyYLl26MGGC60yjU6dOPPaYexD1Jz/5CZ988gnXXHPNSWnF\nxcXx/fff8+KLL/LUU0/xyiuvBL3ep556ihdeeIG+ffty6NAhYmJi+Pzzz/nwww/57rvviI2N5cCB\nAwDccsstPPfcc/Tr14/HHnuMP/zhD4WDYaWnp/PVV+7NiR//+Mc88MADXHrppWzbto3Bgwezdu3a\ncH4tJwk1kNQEnlXVv0Ph2+5B+7gyxpxmSik5RFJ1GtgKOGEQKYA5c+bw5JNPkp2dzYEDB+jcuXPQ\nQHLdddcB0LNnT95///1i0++8WV7jAAAgAElEQVTbty+//vWvGTt2LNdddx2tWrVi1qxZ3H777cTG\nxgLQpEkTMjIySE9PL+xK/tZbb+WGG24oTOemm24qnJ81a9YJY8dnZmaSlZVF/folduBeJqEGki9x\nw+we8pbr4Lp1v6TCcmKMMUVUp4Gt4MRBpHJycvjlL39JcnIyrVu3Zvz48eTk5JR4HaWd7+GHH2b4\n8OF89tln9OnTh1mzZgUd6Ko0/m7zCwoKWLBgAXXq1ClTGmURamN7jKoGggjefGxksmSMMaGpzIGt\nigoEjbi4OA4dOsS0aSGPbFGsTZs2kZiYyLhx40hKSmLdunUMGjSISZMmFTb6HzhwgIYNG9K4cWO+\n/vprACZPnhx0oCvgpIGyAgNvVaRQA8lhEekRWBCRJOBIhefGGGPKoDIHtiqqUaNG3HnnnSQmJjJq\n1Ch69eoV1rWAawgPDGhVp04dhg4dypAhQxgxYgRJSUl07969sBrvjTfe4KGHHqJr164sW7assK2m\nqH/84x8kJyfTtWtXOnXqxMsvvxx2PosKdWCrJOBtXO+9CrQAblLVU+LJLRvYypiysYGtzjzhDGwV\nahtJO+BCoA1u2N0+FD+2iDHGmDNIqIHkUVV9V0QaAQNxb7q/BFwUsZwZY0wlifTAVkW99tprPPvs\nsyes69u37yk7bkmogSTQUjUceFlVPxKR8ZHJkjHGVK5ID2xV1O23337ajI4IoTe27/DGbL8R+Mwb\nJz3UY40xp6BQ2k/N6SHc33WoweBG3ABVQ1Q1HWgCPBTWmY0x1VZMTAz79++3YHIGUFX2799/wrs2\nZRXqeCTZwPu+5V3AruKPMMacylq1akVqaippaWlVnRVTCWJiYgrf9i+PUNtIjDFnkFq1atGuXbuq\nzoY5RUS0nUNEhojIehFJEZGHg2x/WkSWedMGEUn3bcv3bZvuW99ORL4TkY0i8rY3cqMxxpgqErFA\n4nXs+AIwFOgEjBGRTv59VPUBVe2uqt2B5/BVnwFHAttUdYRv/RPA06raHjgI/CxS12CMMaZ0kSyR\n9AZSVHWzqh4DpgIjS9h/DDClpATF9Vx2BRDo1OYNYFQF5NUYY0w5RTKQtAS2+5ZTvXUnEZGzcW/P\nz/atjhGRZBFZKCKBYNEUSFfVQPeZJaV5l3d8sjUYGmNM5ESysT1Yv8fFPUs4Gpimqv4uOtuo6k4R\nOQeYLSIrgcxQ01TVicBEcH1thZ5tY4wxZRHJEkkq0Nq33ArX6WMwoylSraWqO72fm4G5uL6+9gGN\nRCQQAEtK0xhjTCWIZCBZDLT3nrKKxgWL6UV3EpEOQGNggW9dY+/teUQkDugLrFH3dtQc4Hpv11uB\njyJ4DcYYY0oRsUDitWPcg3sjfi3wjqquFpEJIuJ/CmsMMFVPfIX2AiBZRJbjAsdfVDUwVuQ44Nci\nkoJrM3k1UtdgjDGmdCGNR3Kqs/FIjDGm7EIdj8Q6XjTGGBMWCyTGGGPCYoHEGGNMWCyQGGOMCYsF\nEmOMMWGxQGKMMSYsFkiMMcaExQKJMcaYsFggMcYYExYLJMYYY8JigcQYY0xYLJAYY4wJiwUSY4wx\nYbFAYowxJiwWSIwxxoTFAokxxpiwWCAxxhgTFgskxhhjwhLRQCIiQ0RkvYikiMjDQbY/LSLLvGmD\niKR767uLyAIRWS0iK0TkJt8xr4vIFt9x3SN5DcYYY0pWM1IJi0gU8AIwEEgFFovIdFVdE9hHVR/w\n7X8vcKG3mA3coqobRaQFsEREZqhqurf9IVWdFqm8G2OMCV0kSyS9gRRV3ayqx4CpwMgS9h8DTAFQ\n1Q2qutGb3wnsBeIjmFdjjDHlFMlA0hLY7ltO9dadRETOBtoBs4Ns6w1EA5t8q//kVXk9LSK1i0nz\nLhFJFpHktLS08l6DMcaYUkQykEiQdVrMvqOBaaqaf0ICIgnAZOB2VS3wVj8CdAR6AU2AccESVNWJ\nqpqkqknx8VaYMcaYSIlkIEkFWvuWWwE7i9l3NF61VoCINAA+BX6vqgsD61V1lzpHgddwVWjGGGOq\nSCQDyWKgvYi0E5FoXLCYXnQnEekANAYW+NZFAx8A/1bVd4vsn+D9FGAUsCpiV2CMMaZUEXtqS1Xz\nROQeYAYQBUxS1dUiMgFIVtVAUBkDTFVVf7XXjcDlQFMRuc1bd5uqLgPeFJF4XNXZMuDuSF2DMcaY\n0smJ9+/TU1JSkiYnJ1d1Nowx5pQiIktUNam0/ezNdmOMMWGxQGKMMSYsFkiMMcaExQKJMcaYsFgg\nMcYYExYLJMYYY8JigcQYY0xYLJAYY4wJiwUSY4wxYbFAYowxJiwWSIwxxoTFAokxxpiwWCAxxhgT\nFgskxhhjwmKBxBhjTFgskBhjjAmLBRJjjDFhsUBijAmbqnImjLZqgotoIBGRISKyXkRSROThINuf\nFpFl3rRBRNJ9224VkY3edKtvfU8RWeml+Q8RkUhegzGmZGt3ZTLkma8Z+8p3ZObkVnV2TBWIWCAR\nkSjgBWAo0AkYIyKd/Puo6gOq2l1VuwPPAe97xzYBHgcuAnoDj4tIY++wl4C7gPbeNCRS12CMKZ6q\n8u8FWxn5wnz2Hz7Koi0HGDNxIfsOHa3qrJlKFskSSW8gRVU3q+oxYCowsoT9xwBTvPnBwExVPaCq\nB4GZwBARSQAaqOoCdeXofwOjIncJxphg0rOP8fPJS3jso9Vccm5T/vs/l/OvW5PYlHaIG19ewI70\nI1WdRVOJIhlIWgLbfcup3rqTiMjZQDtgdinHtvTmQ0nzLhFJFpHktLS0cl2AMeZki7YcYNizXzNn\n/V5+P/wCJt3ai7h6tRnQoRmTf3YRaYeOcsNL37Ip7VBVZ9VUkkgGkmBtF8W1xo0GpqlqfinHhpym\nqk5U1SRVTYqPjy81s8aYkuUXKM/O2sjoiQuoVbMG7/3iEu647Bxq1Dj+b9mrbROm3tWHY/kF3PDy\nAlbtyKjCHJvKEslAkgq09i23AnYWs+9ojldrlXRsqjcfSprGmAqyK+MIP/7XQp6etYER3Vrwyb2X\n0rVVo6D7dm7RkHd+fjF1akUxeuJCvtu8v5JzaypbJAPJYqC9iLQTkWhcsJhedCcR6QA0Bhb4Vs8A\nBolIY6+RfRAwQ1V3AVki0sd7WusW4KMIXoMxZ7xZa/Yw7NmvWbkjg6du6MbTN3WnfkytEo85J74e\n035xMc0b1OaWSYuYvW5PJeXWVIWIBRJVzQPuwQWFtcA7qrpaRCaIyAjfrmOAqep7CF1VDwB/xAWj\nxcAEbx3AL4BXgBRgE/B5pK7BmDPZ0bx8xk9fzR3/TiahYR0+vvdSru/ZilCfuE9oWId3776E85vX\n565/L+GjZTsinGNTVeRMeIkoKSlJk5OTqzobxpwyNqUd4t63lrJmVya3XdKWR4Z1pHbNqHKllZWT\nyx1vJLNo6wEmjOjMTy5uW7GZNREjIktUNam0/ezN9kqiqszbkMYLc1LYnZFT1dkxJihVZdqSVK55\n7ht2ZRzhlVuSGD+ic7mDCED9mFq88dPeXNmxGY9+tJrnvtxob8GfZmpWdQZOd3n5BXy2ajf//GoT\nq3dmAvCPLzdye992/KLfuTSMLbmu2ZwoN7+A9buzaFovmub1Y054Yuh0lZdfwKa0w7RpEkud6PLf\n0Etz6Ggev/9gJR8u28lF7Zrw7OgLOathTIWkHVMripdu7slvp63gbzM3kHEkl98NvyDkarJwFRQo\n6/dk0aJRHRrWsf+5imaBJEJycvN5N3k7//p6C9sOZHNOfF2e/FFXerZtzAuzU/jnvE289d0P3N3/\nXG6/pF1EbxCnutz8Auan7OOzlbv4Ys0e0rNdNxzRNWvQqnEd2jSJ5ewmsbRuEkubJrG0aRpL68ax\n1K196v95b9iTxYPvLmdFagY1BNo3q0+Xlg1JbNmAxFYN6ZTQsEL+dlakpnPvlKVsP5DNA1edzz1X\nnEdUBQfpWlE1+NsN3WhYpxavfLOFjCO5/Pm6RGpGRa5iZE9mDu8mb2fKou2FL0me3TTW+wzd1KVF\nQ/tCFyZrI6lgGdm5TF64ldfmb2X/4WNc2KYRd/c7l4EXND/h2/PaXZk8NWM9X67bS7P6tbnvyvbc\n1Ks1tSL4T3UqOZZXwPxN+/hshQseGUdyqVe7Jldd0IwBHZuRlZPH9gPZbAtM+7PJOpp3Qhpx9aKP\nBxd/oGkSy1kNqndpJi+/gH/O28yzszZSP6Ym91xxHgcPH2PljgxW7sgs7IakhsB5zeqdcGPs1KIB\nsdGhBdGCAmXS/C088d91xNWrzbOjL6R3uyaRvDRUlWdmbeTZLzcypPNZPDume1hVZ0XlFyjzNqYx\n5bttfLluL/kFSt/zmjKiWwv2HTrGytQMVu7IOOHtewsuwYXaRmKBpILsyjjCq19vYcqibRw+ls+A\nDvHc3e9cerdrUmLxffHWAzzx+TqSfzhI26ax/GZQB4YnJlTJTW5n+hG+WL2bAsX7ttugUr/VB4LH\npyt2MdMLHvVr1+SqTs0ZlpjAZe3jiKkV/IajqmQcyT0eWA5ks/1ANj/sd/M7049Q4PtTj45ypZlA\ncBmaeBaXnBtXSVdasnW7M3no3RWs3JHB8K4JTBjRmab1ahduV1X2ZB71gkoGq7yfaVnHg8u58fXc\nDbFlQ7q2Ch5c9h06yoPvLmfu+jQGdmrOX6/vSqPY6Eq7zknfbGHCJ2voe15TJv4kKey/tT2ZObyz\neDtTF7vSR1y9aK7v2ZrRvVrTNq7uSfsfOHys8LML/Ew9eDy4tGkSW/gZBgLMmRZcLJD4RDKQpOzN\n4uWvNvPRsh0UKFzTNYGf9zuXCxIahJyGqjJ73V6e/O961u/JonOLBvx2SEcubx8X8TrknelH+Gzl\nLj5buYvvt6WfsE2K3JASWzakc4uKDS7H8ly11acrd/HF6t1k5uQVBo/hiQlcdn5chXxbzc0vYGf6\nkZMCzbYD2Wzdl82ho3lc1j6O3w7uSGKrhhVwZeXL48tzN/GP2RtpEFOLP47qwrDEhJCP35OZU/ht\ne2UpwaVx3Vr8+bN1pB/J5ffDL+Anfc6utPYKv2lLUhn33goSWzbk9dt7lTmQ5Re4h1jeWrSN2V7p\n49Lz4hjTuw0DOzUnumbZSvgHDx9j1c4Tg8v2A8UHlyZ1KzbwRtcUGsTUokGdWsV+aapMFkh8IhFI\nlvxwkJe/2sTMNXuIqVWD0b3a8LNL29G6SWy508wvUKYv38HfvthA6sEj9DmnCeOGdOTCNo1LP7gM\ndqQf4fOVu/h05S6WesGjU0IDhndNYFhiAnVrR7FqRwYrUo//M+3JdDckETgnru6JwaVlQ+qVIbgc\nyyvgm5Q0Pl2xm5lrjgePgYGSRwUFj1Dl5Obzn4U/8MKcFA5m5zK8awK/GXg+58TXq7Q8rN2VyUPT\nlrNqRybXdGvBH0Z0rpCblD+4BH6Xe73gck58XZ4f04NOLUL/0hMJM1bv5t63ltI2LpbJP7uI5g1K\nb+DfnZHD24u38/bibezMyCGuXjQ3JLnSx9lNTy59hKO04BIptWvWoGGdWidNDYKsaxh74nJFBSEL\nJD4VFUhUlTnr9/Ly3M0s2nqARrG1uPXittx6SdsK/WZyNC+fKd9t47nZKew/fIzBnZvz4KAOtG9e\nv9xpph7M5vOVu/l05S6WbXfBo3OLBgxLTGB4YkLQor/f3qwc90+Umln4D7U70z3GLALtvOASCDCd\nWzQ44e3nQPD4xKu2ysrJo36MCx7DExO4tH3lBo9gMnNyeWXeZl75ZgtH8wq4qVdr7r+yfUg3tvLK\nzS/gpbmbeG72RhrWqcX/jurCkC6hl0LKY29mDpvSDtOtdcOQ21Ii7duUfdz572Sa1IvmzZ/1oU3T\nk7+Q5RcoX23Yy1vfbWf2uj0UKFzWPo4f927DlReUvfQRjvTsY6zemUlWTl7pO5fBsfwCMo7kknkk\nl4wjuWRkez99U+aR3JPaA4uK9gWhV25JKvX/uzgWSHzCDSS5+QV8vHwn//xqs3uEsGEMd1x2DqN7\nt47oP+Kho3lM+mYLE+dtJvtYHj/q0Yr/GXg+LRvVCen44oLH8K4JDOtSevAoTanBpWldurRsSM0a\nwsy1JwaPq7sm0Pe8qg8ewaRlHeX52Rt5a9E2aohE7FHtNTtdKWT1zkxGdGvB+AoqhZyqlm1P57bX\nFhEdVYPJP7uIDme5L067Mo7w9uLtvLN4u1f6qM2NSa0Y3atN0IBzJsjLLyArJ++kIOMPNoH5CSO7\nEF+/dumJBmGBxKe8gST7WB5vL97OK19vYUf6ETo0r8/P+53DNd1aVOrTVQcOH+PFOSn8e8EPAPzk\n4rP51YDzgt50th/I5vNVu/h05W6We8GjS0tX8qiI4FGatKyjhcX/QHA5fDSPgZ3OYnjXs6pt8Ahm\n2/5snp61gQ+X7aB+7ZoV9qh2bn4BL85xpZBGsdFeKeSsCsr1qW3jnixufvU7cnILeGhwB+au38vs\ndXtR4LL28fy4d2uuvKC5Pd1YSSyQ+JQ3kNz48gIWbT1Ar7aN+UX/cxnQoVmVNEgG7Eg/wjMzN/De\n96nERtfkrsvP4WeXtuPA4WOFDebLU1233YHgMTwxocLrjM80Ffmo9uqdGTz07grW7MpkZPcWjL+m\nM43P4FJIMNsPZHPzq9/xw/5s4uvX5qak1tzUq3VY7Y+mfCyQ+JQ3kHybso/atWrQ8+zIPldfVhv3\nZPHUF+uZsXoPsdFRZB9zw7gktmzoSh6JZ1nwiIBwHtU+llfAC3NSeGFOCo1io/nTtV0Y3NlKIcUJ\nPJp78blNrfRRhSyQ+JyunTZ+v+0gby7cxnnN6jE8MeGMrS+uTOV5VHvVjgwemraCtbsyufbCljx+\nTadKfV/DmPKyQOJzugYSU3VCeVT7WF4Bz8/eyItzN9G4bjT/d20iAzs1r8JcG1M2Fkh8LJCYSDmW\nV8CURdt4bvZG9h06/qj20bwCHnx3Oet2Z3HdhS15zEoh5hRkgcTHAomJtMNH83jV96i2iNDUK4Vc\nZaUQc4oKNZBUj7eRjDnF1a1dk/uubM/Nfc5m4rzN5OYXcN8V7c+4vpnMmckCiTEVqEndaB4e2rGq\ns2FMpYroc3UiMkRE1otIiog8XMw+N4rIGhFZLSJveesGiMgy35QjIqO8ba+LyBbftu6RvAZjjDEl\ni1iJRESigBeAgUAqsFhEpqvqGt8+7YFHgL6qelBEmgGo6hygu7dPEyAF+MKX/EOqOi1SeTfGGBO6\nSJZIegMpqrpZVY8BU4GRRfa5E3hBVQ8CqOreIOlcD3yuqtkRzKsxxphyimQgaQls9y2neuv8zgfO\nF5H5IrJQRIYESWc0MKXIuj+JyAoReVpEgvZGJiJ3iUiyiCSnpaWV9xqMMcaUIpKBJNhrvkWfNa4J\ntAf6A2OAV0SkUWECIglAIjDDd8wjQEegF9AEGBfs5Ko6UVWTVDUpPj6+vNdgjDGmFJEMJKlAa99y\nK2BnkH0+UtVcVd0CrMcFloAbgQ9UNTewQlV3qXMUeA1XhWaMMaaKRDKQLAbai0g7EYnGVVFNL7LP\nh8AAABGJw1V1bfZtH0ORai2vlIK4jo1GAasikntjjDEhidhTW6qaJyL34KqlooBJqrpaRCYAyao6\n3ds2SETWAPm4p7H2A4hIW1yJ5qsiSb8pIvG4qrNlwN2RugZjjDGlOyO6SBGRNOCHch4eB+yrwOxU\ndHqRSPNMSy8SaVb39CKRZnVPLxJpnu7pna2qpTYynxGBJBwikhxKXzNVlV4k0jzT0otEmtU9vUik\nWd3Ti0SaZ1p6xbERY4wxxoTFAokxxpiwWCAp3cRqnl4k0jzT0otEmtU9vUikWd3Ti0SaZ1p6QVkb\niTHGmLBYicQYY0xYLJAYY4wJiwWSEoQynkoZ0pokIntFpELexBeR1iIyR0TWemO53F8BacaIyCIR\nWe6l+YcKymuUiCwVkU8qIK2tIrLSG4sm7PGTRaSRiEwTkXXeZ3lxmOl1KDKWTqaI/E+YaT7g/T5W\nicgUEYkJM737vbRWlzdvwf6eRaSJiMwUkY3ez8ZhpneDl8cCESnTI6zFpPdX7/e8QkQ+8PfrF0aa\nf/TSWyYiX4hIi3DS8217UETU6/EjnPyNF5Edvr/HYaGmVyaqalOQCfc2/ibgHCAaWA50CiO9y4Ee\nwKoKyl8C0MObrw9sCCd/XjoC1PPmawHfAX0qIK+/Bt4CPqmAtLYCcRX4e34DuMObjwYaVfDf0G7c\nS13lTaMlsAWo4y2/A9wWRnpdcN0KxeJ6tpgFtC9HOif9PQNPAg978w8DT4SZ3gVAB2AukFQB+RsE\n1PTmnyhL/kpIs4Fv/j7g5XDS89a3xvX68UNZ/taLyd944MGK+HsuabISSfFCGU8lZKo6DzhQUZlT\n13nl9958FrCWk7vpL2uaqqqHvMVa3hTW0xgi0goYDrwSTjqRICINcP98rwKo6jFVTa/AU1wJbFLV\n8vaqEFATqCMiNXEBoGjnp2VxAbBQVbNVNQ/XBdG1ZU2kmL/nkbjAjPdzVDjpqepaVV1f1ryVkN4X\n3jUDLMR1JBtumpm+xbqU4f+lhHvC08Bvy5JWKelFnAWS4oUynkq14PVLdiGuBBFuWlEisgzYC8xU\n1XDTfAb3T1EQbt48CnwhIktE5K4w0zoHSANe86reXhGRuuFnsVCwsXTKRFV3AE8B24BdQIaqflHy\nUSVaBVwuIk1FJBYYxom9dIejuaruAvdFB2hWQelGwk+BzysiIRH5k4hsB8YCj4WZ1ghgh6our4i8\nee7xqt8mlaW6sSwskBQvlPFUqpyI1APeA/6nyLejclHVfFXtjvu21ltEuoSRt6uBvaq6JNx8+fRV\n1R7AUOBXInJ5GGnVxFUFvKSqFwKHcVUyYRPX4/UI4N0w02mM+6bfDmgB1BWRm8ubnqquxVXrzAT+\ni6uyzSvxoNOMiPwOd81vVkR6qvo7VW3tpXdPGPmKBX5HmMGoiJeAc3FDl+8C/laBaReyQFK8UMZT\nqVIiUgsXRN5U1fcrMm2vimcuEGzUylD1BUaIyFZc1eAVIvKfMPO10/u5F/iA8MajSQVSfaWuabjA\nUhGGAt+r6p4w07kK2KKqaerG5XkfuCScBFX1VVXtoaqX46pCNoaZx4A9cnyYhwRcqbZaEZFbgauB\nseo1IlSgt4AfhXH8ubgvDMu9/5lWwPciclZ5E1TVPd6XwwLgX0Ro/CYLJMULZTyVKiMigqvbX6uq\nf6+gNOMDT7KISB3cTWxdedNT1UdUtZWqtsV9frNVtdzfpkWkrojUD8zjGk/L/RScqu4GtotIB2/V\nlcCa8qZXxElj6ZTTNqCPiMR6v/Mrce1h5SYizbyfbYDrqJh8gvv/uNWbvxX4qILSrRDihvIeB4xQ\n1ewKStM/EN8Iwvt/WamqzVS1rfc/k4p7oGZ3GPlL8C1eS6TGb4p0a/6pPOHqjzfgnt76XZhpTcEV\nLXNxfyA/CzO9S3FVbStw47IsA4aFmWZXYKmX5irgsQr8LPsT5lNbuDaN5d60OtzfiZdmdyDZu+YP\ngcYVkGYssB9oWEGf3R9wN6hVwGSgdpjpfY0LmMuBK8uZxkl/z0BT4EtcCedLoEmY6V3rzR8F9gAz\nwkwvBdfuGfh/CfkJqxLSfM/7vawAPgZahpNeke1bKdtTW8HyNxlY6eVvOpBQEX+TRSfrIsUYY0xY\nrGrLGGNMWCyQGGOMCYsFEmOMMWGxQGKMMSYsFkiMMcaExQKJMcaYsFggMWcsEelfWtf2oewTwnnu\nFDccwWoR+WU4aZXz/FvL0h15CencJiLPh7DfeBF5MNzzmVNHzarOgDHBeG9xi7quHU5ZXo+9fwLO\nA7KAs0M9To/3VGtMtWYlElNtiEhbcYNLvQh8D7QWkUEiskBEvheRd71OKhGRYd4gRd+IyD9KKjWI\nSG8R+dbr4fdbX5co/n3Gi8hkEZktbmCmO32b68nxwa/e9IIcIvKYiCwWN0jUxMD6IGoCTdXZWkI+\nXxeRv4vIHOAJr0uYSd45lorISG+/WBF5x+vR9W0R+U5CHPhJRD70ek5e7e89WUQOicgT3rZZ3mc2\nV0Q2ez3SBrQWkf96JazHfcf/zls3CzeGSGD9nV7+l4vIe17HhOZ0E4nX5W2yqTwT0BbX3XwfbzkO\nmAfU9ZbH4XpGjcF1ddHOWz+FErpfARpwfECjq4D3vPn+geNwAwAtB+p4592O6223P5CB60CvBrAA\nuNQ7ponvHJOBa4KcOwbX+eVySukyBHgd+ASI8pb/D7jZm2+E666nLvAg8E9vfRdcT7bFDvyEr6uN\nQB6861yFC3DgutsZ6s1/AHyBG4+mG7DMW38brguOpr7jk4CeuG44Yr3POgVvMKVA+t78/wL3VvXf\nmU0VP1nVlqluflDVhd58H6ATMN/7sh+Nu5F3BDar6hZvvylASWOTNATe8DrYU9wNMpiPVPUIcMQr\nFfQG0oFFqpoKIG6slrbAN8AAEfkt7gbaBNf/18dF0vwzLsjkAh+LyEBc77O9VPWhIHl4V1XzvflB\nuN6TA+0NMUAbXD9rzwKo6ioRWVHCtRd1n4gEBrJqDbTH9Qt2DNetPLigcFRVc0VkpXe9ATNVdb/3\nWbzv5QXgA/U6QhQRf+emXUTkf3GBsB5u5D9zmrFAYqqbw755wd24xvh3EJELy5jmH4E5qnqtuEHA\n5hazX9GO5wLLR33r8oGa4sZNfxFXEtguIuNxN/qiBgPPqupWr9fdd3HX+Ndi8lD0+n+kRUYJLKEK\nrUQi0h9XIrtYVbNFZK4vz7mqGrjeArxrVtUCr50nINhnJEHWB7wOjFLV5SJyG66EZ04z1kZiqrOF\nQF8ROQ8K2wbOx/WEe44XFABuKiWdhsAOb/62EvYbKSIxItIUd8NbXMK+gRvwPq/d5vpi9lsK3OLN\n/x2oD3QGQhnsawZwr69NJhBAvwFu9NZ1AhJDSAvc53DQCyIdcSW+shooIk3EDTMwCpiPq368VkTq\niOvm/xrf/vWBXeLGzs5nA18AAAFWSURBVBlbjvOZU4AFElNtqWoa7sY/xau+WQh09Kqffgn8V0S+\nwXUxnlFCUk8CfxaR+UBUCfstAj71zvNH9QbRKiZv6biBglbiup8vLuj8D9BdRFZ76c/w9n26hHwE\n/BFXDbdCRFZ5y+BKQvHeZzIO10V4Sdcf8F9caWqFl9bCUvYP5htcVd0yXFtTsqp+D7wdWIfrpj7g\nUdwQ0DMJY6wOU71ZN/LmlCQi9VT1kPdt/QVgo6qGcnMuLr3xwCFVfaqi8hgpIhIF/7+9O8RBIAiC\nKFolgIRrkHArBAJBEJyH06xCrN7LEEwhBrW2k53A/idblatMj2htkrxsH9RufxyTvDtHw0rxR4Jf\ndXE7m7pVWx89OudZ0l7S8F0XWdKVEkFPvEjwN2yfJd1n42eSW488S7M9StrNxqckU488WA+KBABQ\nwmc7AKCEIgEAlFAkAIASigQAUPIBYWxtFeXhhroAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a0a45cac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 画图观察准确率和 L1(reg_alpha) 的关系\n",
    "mean_test_scores = grid_reg2.cv_results_['mean_test_score']\n",
    "mean_train_scores = grid_reg2.cv_results_['mean_train_score']\n",
    "\n",
    "plt.plot(range(0, len(mean_test_scores)), mean_test_scores, label = 'mean_test_score')\n",
    "plt.plot(range(0, len(mean_train_scores)), mean_train_scores, label = 'mean_train_score')\n",
    "plt.xticks(range(0, len(mean_test_scores)))\n",
    "plt.title('reg_alpha_lambda & scores')\n",
    "plt.xlabel('reg_alpha & reg_lambda')\n",
    "plt.ylabel('scores')\n",
    "plt.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GridSearchCV_reg 测试集评价结果： 0.66\n"
     ]
    }
   ],
   "source": [
    "# 测试集上的评价结果\n",
    "print('GridSearchCV_reg 测试集评价结果：', grid_reg2.best_estimator_.score(X_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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pI6U7JTUFGpvZK5LeISzZKPVLSeOA/YEOhKUb8RX3mgOfRduFCfd9nPCw2psW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xcP/z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\nB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AEcdthhXHHFFWy++eaVbpKZmZlVIc8wV6engGMk/RJYCFwFbA48DiwCHiooex1wdcFL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\nREREjf8AsZCo0XRRX2UAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1013b6a20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 观察特征重要性\n",
    "#grid.best_estimator_.feature_importances_\n",
    "fig, ax = plt.subplots(figsize=(10, 15))\n",
    "xgb.plot_importance(grid_reg2.best_estimator_.fit(X_train, y_train), ax = ax)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "特征重要性有变化"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 重新调整弱学习器数目"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=0.7,\n",
       "       colsample_bytree=0.8, gamma=0, learning_rate=0.1, max_delta_step=0,\n",
       "       max_depth=7, min_child_weight=1, missing=None, n_estimators=34,\n",
       "       n_jobs=1, nthread=None, objective='multi:softprob', random_state=0,\n",
       "       reg_alpha=0.5, reg_lambda=0.5, scale_pos_weight=1, seed=3,\n",
       "       silent=True, subsample=0.3)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xgb_c.set_params(n_estimators = best_estimator, reg_alpha = 0.5, reg_lambda = 0.5, max_depth = 7, min_child_weight = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>test-mlogloss-mean</th>\n",
       "      <th>test-mlogloss-std</th>\n",
       "      <th>train-mlogloss-mean</th>\n",
       "      <th>train-mlogloss-std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.050214</td>\n",
       "      <td>0.003943</td>\n",
       "      <td>1.038558</td>\n",
       "      <td>0.002134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.008501</td>\n",
       "      <td>0.008361</td>\n",
       "      <td>0.985342</td>\n",
       "      <td>0.004246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.970244</td>\n",
       "      <td>0.009911</td>\n",
       "      <td>0.939589</td>\n",
       "      <td>0.006081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.938256</td>\n",
       "      <td>0.010897</td>\n",
       "      <td>0.898309</td>\n",
       "      <td>0.004771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.910236</td>\n",
       "      <td>0.011102</td>\n",
       "      <td>0.859962</td>\n",
       "      <td>0.006493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.885464</td>\n",
       "      <td>0.013045</td>\n",
       "      <td>0.825377</td>\n",
       "      <td>0.005855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.866616</td>\n",
       "      <td>0.013688</td>\n",
       "      <td>0.794808</td>\n",
       "      <td>0.006088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.850470</td>\n",
       "      <td>0.013504</td>\n",
       "      <td>0.767141</td>\n",
       "      <td>0.005909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.835839</td>\n",
       "      <td>0.013948</td>\n",
       "      <td>0.741880</td>\n",
       "      <td>0.005729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.821083</td>\n",
       "      <td>0.013614</td>\n",
       "      <td>0.719023</td>\n",
       "      <td>0.005635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.809093</td>\n",
       "      <td>0.011819</td>\n",
       "      <td>0.696656</td>\n",
       "      <td>0.006206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.796276</td>\n",
       "      <td>0.012301</td>\n",
       "      <td>0.676384</td>\n",
       "      <td>0.006493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.786734</td>\n",
       "      <td>0.012833</td>\n",
       "      <td>0.657596</td>\n",
       "      <td>0.006405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.777581</td>\n",
       "      <td>0.013864</td>\n",
       "      <td>0.641344</td>\n",
       "      <td>0.006761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.770361</td>\n",
       "      <td>0.014732</td>\n",
       "      <td>0.625020</td>\n",
       "      <td>0.007359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.763632</td>\n",
       "      <td>0.014188</td>\n",
       "      <td>0.610041</td>\n",
       "      <td>0.006725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.758122</td>\n",
       "      <td>0.016354</td>\n",
       "      <td>0.595746</td>\n",
       "      <td>0.006758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.750885</td>\n",
       "      <td>0.016930</td>\n",
       "      <td>0.582908</td>\n",
       "      <td>0.006660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.747130</td>\n",
       "      <td>0.016725</td>\n",
       "      <td>0.569897</td>\n",
       "      <td>0.006487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.743592</td>\n",
       "      <td>0.017357</td>\n",
       "      <td>0.558538</td>\n",
       "      <td>0.005785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.739008</td>\n",
       "      <td>0.017283</td>\n",
       "      <td>0.547368</td>\n",
       "      <td>0.005308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.736322</td>\n",
       "      <td>0.018937</td>\n",
       "      <td>0.537379</td>\n",
       "      <td>0.004926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.733397</td>\n",
       "      <td>0.019197</td>\n",
       "      <td>0.526941</td>\n",
       "      <td>0.004489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0.732428</td>\n",
       "      <td>0.019161</td>\n",
       "      <td>0.517424</td>\n",
       "      <td>0.004737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0.729299</td>\n",
       "      <td>0.017735</td>\n",
       "      <td>0.508312</td>\n",
       "      <td>0.004546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0.727703</td>\n",
       "      <td>0.017048</td>\n",
       "      <td>0.499169</td>\n",
       "      <td>0.004625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0.725705</td>\n",
       "      <td>0.017765</td>\n",
       "      <td>0.491185</td>\n",
       "      <td>0.004374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0.724434</td>\n",
       "      <td>0.018489</td>\n",
       "      <td>0.483301</td>\n",
       "      <td>0.004836</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0.722532</td>\n",
       "      <td>0.019779</td>\n",
       "      <td>0.475324</td>\n",
       "      <td>0.004081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0.722245</td>\n",
       "      <td>0.019741</td>\n",
       "      <td>0.467683</td>\n",
       "      <td>0.004470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>0.719901</td>\n",
       "      <td>0.020198</td>\n",
       "      <td>0.460344</td>\n",
       "      <td>0.004583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>0.718387</td>\n",
       "      <td>0.020852</td>\n",
       "      <td>0.453111</td>\n",
       "      <td>0.003711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>0.718532</td>\n",
       "      <td>0.021394</td>\n",
       "      <td>0.446836</td>\n",
       "      <td>0.003588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>0.718133</td>\n",
       "      <td>0.021083</td>\n",
       "      <td>0.439641</td>\n",
       "      <td>0.003484</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    test-mlogloss-mean  test-mlogloss-std  train-mlogloss-mean  \\\n",
       "0             1.050214           0.003943             1.038558   \n",
       "1             1.008501           0.008361             0.985342   \n",
       "2             0.970244           0.009911             0.939589   \n",
       "3             0.938256           0.010897             0.898309   \n",
       "4             0.910236           0.011102             0.859962   \n",
       "5             0.885464           0.013045             0.825377   \n",
       "6             0.866616           0.013688             0.794808   \n",
       "7             0.850470           0.013504             0.767141   \n",
       "8             0.835839           0.013948             0.741880   \n",
       "9             0.821083           0.013614             0.719023   \n",
       "10            0.809093           0.011819             0.696656   \n",
       "11            0.796276           0.012301             0.676384   \n",
       "12            0.786734           0.012833             0.657596   \n",
       "13            0.777581           0.013864             0.641344   \n",
       "14            0.770361           0.014732             0.625020   \n",
       "15            0.763632           0.014188             0.610041   \n",
       "16            0.758122           0.016354             0.595746   \n",
       "17            0.750885           0.016930             0.582908   \n",
       "18            0.747130           0.016725             0.569897   \n",
       "19            0.743592           0.017357             0.558538   \n",
       "20            0.739008           0.017283             0.547368   \n",
       "21            0.736322           0.018937             0.537379   \n",
       "22            0.733397           0.019197             0.526941   \n",
       "23            0.732428           0.019161             0.517424   \n",
       "24            0.729299           0.017735             0.508312   \n",
       "25            0.727703           0.017048             0.499169   \n",
       "26            0.725705           0.017765             0.491185   \n",
       "27            0.724434           0.018489             0.483301   \n",
       "28            0.722532           0.019779             0.475324   \n",
       "29            0.722245           0.019741             0.467683   \n",
       "30            0.719901           0.020198             0.460344   \n",
       "31            0.718387           0.020852             0.453111   \n",
       "32            0.718532           0.021394             0.446836   \n",
       "33            0.718133           0.021083             0.439641   \n",
       "\n",
       "    train-mlogloss-std  \n",
       "0             0.002134  \n",
       "1             0.004246  \n",
       "2             0.006081  \n",
       "3             0.004771  \n",
       "4             0.006493  \n",
       "5             0.005855  \n",
       "6             0.006088  \n",
       "7             0.005909  \n",
       "8             0.005729  \n",
       "9             0.005635  \n",
       "10            0.006206  \n",
       "11            0.006493  \n",
       "12            0.006405  \n",
       "13            0.006761  \n",
       "14            0.007359  \n",
       "15            0.006725  \n",
       "16            0.006758  \n",
       "17            0.006660  \n",
       "18            0.006487  \n",
       "19            0.005785  \n",
       "20            0.005308  \n",
       "21            0.004926  \n",
       "22            0.004489  \n",
       "23            0.004737  \n",
       "24            0.004546  \n",
       "25            0.004625  \n",
       "26            0.004374  \n",
       "27            0.004836  \n",
       "28            0.004081  \n",
       "29            0.004470  \n",
       "30            0.004583  \n",
       "31            0.003711  \n",
       "32            0.003588  \n",
       "33            0.003484  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 再次调用xgb的cv来调整弱学习器数目\n",
    "params = xgb_c.get_xgb_params()\n",
    "params['num_class'] = 3\n",
    "xgb_cv2 = xgb.cv(params = params, \n",
    "                dtrain = xgb.DMatrix(X_train, label = y_train), \n",
    "                num_boost_round = xgb_c.get_params()['n_estimators'], nfold=5,\n",
    "                metrics = 'mlogloss')\n",
    "\n",
    "xgb_cv2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "best n_estimator:  33\n"
     ]
    }
   ],
   "source": [
    "# 通过cv结果获得测试集上误差最小时的n_estimator\n",
    "test_logloss_sort2 = pd.Series(xgb_cv2['test-mlogloss-mean']).sort_values()\n",
    "best_estimator2 = list(test_logloss_sort2.keys())[0]\n",
    "print ('best n_estimator: ', best_estimator2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 行列重采样参数调整\n",
    "subsample 构造每棵树所用样本比例，建议0.3～0.8      \n",
    "colsample_bytree 构造每棵树所用特征比例，建议0.3～0.8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GridSearchCV_sample 训练集最佳评价结果： 0.6975\n",
      "最佳参数： {'colsample_bytree': 0.7, 'subsample': 0.7}\n"
     ]
    }
   ],
   "source": [
    "xgb_c.set_params(n_estimators = best_estimator2)\n",
    "grid_sample1 = GridSearchCV(xgb_c, dict(subsample = [0.3, 0.4, 0.5, 0.6, 0.7], colsample_bytree = [0.3, 0.4, 0.5, 0.6, 0.7]), cv = 5)\n",
    "grid_sample1.fit(X_train, y_train)\n",
    "\n",
    "print('GridSearchCV_sample 训练集最佳评价结果：', grid_sample1.best_score_)\n",
    "print('最佳参数：', grid_sample1.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GridSearchCV_sample 训练集最佳评价结果： 0.69875\n",
      "最佳参数： {'colsample_bytree': 0.7, 'subsample': 0.8}\n"
     ]
    }
   ],
   "source": [
    "# 缩小参数的取值范围\n",
    "grid_sample2 = GridSearchCV(xgb_c, dict(subsample = [0.65, 0.7, 0.75, 0.8, 0.85], colsample_bytree = [0.65, 0.7, 0.75, 0.8, 0.85]), cv = 5)\n",
    "grid_sample2.fit(X_train, y_train)\n",
    "\n",
    "print('GridSearchCV_sample 训练集最佳评价结果：', grid_sample2.best_score_)\n",
    "print('最佳参数：', grid_sample2.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GridSearchCV_sample 测试集评价结果： 0.68\n"
     ]
    }
   ],
   "source": [
    "# 测试集上的评价结果\n",
    "print('GridSearchCV_sample 测试集评价结果：', grid_sample2.best_estimator_.score(X_test, y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "经过参数的调整，测试集上的评价结果变好"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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TBoC+ffsyefJkzIx58+bRsGFDn47hXBnxOcwpSNIhwBPAGDOzcLrFl2FBlUuB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\n4KyzzmLMmDHstdde9d4fERERyR4aMMNVQKG7F5nZWcAg4HCgFfCumb0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3tN//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\nJEmSOhgwS5IkSR0MmCVJkqQOfwHJPLTWKvjWCwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a0a5671d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 观察特征重要性\n",
    "#grid.best_estimator_.feature_importances_\n",
    "fig, ax = plt.subplots(figsize=(10, 15))\n",
    "xgb.plot_importance(grid_sample2.best_estimator_.fit(X_train, y_train), ax = ax)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=0.7,\n",
       "       colsample_bytree=0.7, gamma=0, learning_rate=0.1, max_delta_step=0,\n",
       "       max_depth=7, min_child_weight=1, missing=None, n_estimators=33,\n",
       "       n_jobs=1, nthread=None, objective='multi:softprob', random_state=0,\n",
       "       reg_alpha=0.5, reg_lambda=0.5, scale_pos_weight=1, seed=3,\n",
       "       silent=True, subsample=0.8)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 更新模型参数\n",
    "xgb_c.set_params(subsample = 0.8, colsample_bytree = 0.7)\n",
    "xgb_c"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 特征选择并测试模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(800, 37) [  0   1   2   3   4   5   6   9  10  11  12  13  15  20  21  22  23  24\n",
      "  25  26  35  57  74  82  83  84  85  91  96 101 108 113 116 128 151 162\n",
      " 211]\n",
      "37 ['bathrooms', 'bedrooms', 'price', 'price_bathrooms', 'price_bedrooms', 'room_diff', 'room_num', 'Day', 'Wday', 'Yday', 'hour', 'top_10_manager', 'top_5_manager', 'top_20_manager', 'top_30_manager', 'cenroid', 'distance', 'display_address_pred_0', 'display_address_pred_1', 'display_address_pred_2', 'allowed', 'building', 'common', 'dining', 'dishwasher', 'dogs', 'doorman', 'elevator', 'fee', 'floors', 'garden', 'hardwood', 'high', 'laundry', 'outdoor', 'pre', 'unit']\n"
     ]
    }
   ],
   "source": [
    "# 特征选择\n",
    "from sklearn.feature_selection import SelectFromModel\n",
    "xgb_c.fit(X_train, y_train)\n",
    "selection = SelectFromModel(xgb_c, prefit = True)\n",
    "select_X_train = selection.transform(X_train)\n",
    "select_X_train = pd.DataFrame(select_X_train)\n",
    "select_X_train.columns = vector_names\n",
    "print (select_X_train.shape, selection.get_support(indices=True))\n",
    "\n",
    "# 1st way to get the list\n",
    "columns = X.keys()\n",
    "vector_names = list(X.columns[selection.get_support(indices=True)])\n",
    "print(len(vector_names), vector_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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FhSWw09PTGTBgQGGf+Ph4vvjiCxo2bMjChQvZu3cv6enp7N69m927d3PTTTfR\nsGHDSN1Cualo5Z4rssoaq+qWMAeWJir4fDCwIyBB+he0D1HV+ad6TRE5GxgGdFTVfe70ijNCHH9M\nT5RUyqfo35n/uIWTaZh2cKaZ4E4R8b/OcSBKVfNEpBPOVJSbgcHAxSHGaYwxpgrJyspi4cKF7Nq1\nq0gJ7FAV/R577DEyMjJISUlBVXnmmWeqRbJsqofqljA3F5ELVXUp0Bf4F84c4GC+AZqISEd3SkY9\nnCkZ84E7RWSR+3T2XGC7qgZLugE6uQnyVqAPzpPY+jjJ7k8i0hhn3rDP7X8AqAf8cPKpwvoMuAV4\nQkTSgR9U9Wd3WkWw9mJPKCLRQB1VnSsiy4BNJRyTMcaYSqpz584sXrw47NPAnJycwu0mTZoEndts\nTFVQ3RLm9UB/EXkR2AhMBoYE66iqv4hIH+B5EamNkyxfCryCM11hlThZ5x7gujDXXIrzkmAKTlL7\nrvtEdzXwNbAZyPLr/xLwTxHZWfDSn0ejgNdEZA3Oy339i2n3oh4wR0TOwHlSfV8JjjXGGGOMqRKq\nW8J8XFUHBbTF+39Q1Qy/7c9xVo4I9Ij7FZaq+jjx5DhwX0aI9ueB5/0+R/ttz8aZ/3zS8aq6Fzhp\n8nCY9lEBn6ND7OsUbJzGGGOMMdWFLZBojDHGGGNMGNUmYVbVHFVNLotzi0iKiGQHfC0vi2sZY4yp\nXrZt20b37t1JTEwkKSmJCRMmAPDAAw/QunVr2rZty/XXX8/+/fsBOHbsGP379yclJYXExESefvrp\nSA7fmCqh2iTMZUlV16pqasBXWqTHJSLxIvJVpMdhjDHm1EVFRTFu3DjWr1/PsmXLmDRpEuvWraNH\njx589dVXrFmzhnPPPbcwMZ41axZHjx5l7dq1rFy5khdffLHIy3nGmJKzhNmUiIhUt3nvxhgTUXFx\ncbRv3x6AevXqkZiYyPbt27nsssuIinK+JV9wwQV89913AIgIBw8eJC8vj8OHD1OzZk2rtmfMr2TJ\nT9VXQ0ReBn6PU/L6WpyiLC8AdYD/AAPc9aB9wDBV/UJEGgJfqGq8iGQAV+GsFV2XMGsxW2lsb6w0\nr3cWK28sTt5VllgFlqgGZxm31atXk5ZW9JeYU6ZMoU+fPgDceOONzJkzh7i4OA4dOsT48eOJjY0t\nlzEbU1XZE+aqrxUwSVWTgP04Vf9eBx5S1bY4Vf3+4uE8FwL9VdUKlxhjTATk5ubSq1cvMjMzizwx\nHjNmDFFRUdxyyy0ArFixgho1arBjxw62bNnCuHHj2Lx5c6SGbUyVYE+Yq74tqprtbq8EzgEaqOqn\nbts0YJaH8yx0l6g7iVvi+w7DhjBUAAAgAElEQVSAhg3PYmRK3q8cctXXuLbzlMsUz2LljcXJu8oS\nK5/PV7idl5fHww8/TFpaGrGxsYX75s2bxwcffMC4ceP49FPn23pmZiZt2rQhK8tZ4r9FixZMmzaN\n7t1LsrS/Izc3t8g4THAWJ+8qa6wsYa76jvpt5wMNwvTN48RvHQJLdYeqZIiqvoRTcIWEhAQdcstJ\nyz6bAD6fj95hqmeZEyxW3licvKtssVJV+vfvz0UXXURmZmZh+7x583j//ff59NNPOeusswrbly9f\nzjfffEO3bt04dOgQW7du5ZlnnqFt27YlvrbP5wtb6c84LE7eVdZY2ZSM6ucnYJ+IdHE/3woUPG3O\nATq42zeW87iMMcYEkZWVxRtvvMGiRYtITU0lNTWVuXPnMnjwYA4cOECPHj1ITU1l0CCnLtddd91F\nbm4uycnJdOzYkdtuu+2UkmVjzAn2hLl66g+8ICJ1cEpz3+a2Pwe8LSK3AosiNThjjDEndO7cGVU9\nqf3KK68M2j86OppZs7zMtDPGeGUJcxWmqjlAst/n5/x2n1TyW1W/AfwfQzzqtk8FppbFGI0xxhhj\nKjqbkmGMMcYYY0wYljAbY4wxQYQqSb1371569OhBq1at6NGjB/v27QOcl5liYmIK5xmPHj06ksM3\nxpQiS5irGHH8S0Su8GvrLSLzAvqNEpFh5T9CY4ypHEKVpB47diyXXHIJGzdu5JJLLmHs2LGFx3Tp\n0oXs7Gyys7MZOXJkBEdvjClNljBXMeq8GTII+KuInCEidYExwF2RHZkxxlQuoUpSz5kzh/79+wPQ\nv39/3nvvvUgO0xhTDixhroJU9SvgA+AhnCp+r6vqf0RkhIhsEJGPccpjAyAifxKRz0XkSxF5R0Tq\niEg9EdkiIqe7feqLSE7BZ2OMqU78S1Lv3r2buLg4wEmqv//++8J+S5cupV27dlxxxRV8/fXXkRqu\nMaaU2SoZVdfjwCrgF+B8EekA3Aych/P3vgqn8h/A/6nqywAi8iQwUFWfFxEfcBXwnnvsO6p6LNxF\nDx/LJ374R2VwO1XL0JQ8MixOnlisvLE4eVdcrHLGXlXkc6iS1IHat2/P1q1biY6OZu7cuVx33XVs\n3Lix1MZtjIkcS5irKFU9KCIzgVxVPeoWKnlXVQ8BiMj7ft2T3US5ARANzHfbXwEexEmYbwP+FOxa\nVhq75CpLad6KwGLljcXJu+JiVVxJ6vr16/POO+9w5pln8uOPP1KvXr2TSv3WqVOHAwcOMGfOHGJi\nYsroTspeZS1jXN4sTt5V1lhZwly1HXe/Cpy88r1jKnCdqn4pIhlAOoCqZolIvIh0A2q4Uz1OYqWx\nS66yleaNJIuVNxYn77zGKlRJ6j59+rBx40Z69erF2LFjufnmm0lPT2fXrl00btwYEWHFihXUrFmT\nnj17IiJleDdlq7KWMS5vFifvKmusLGGuPj4DporIWJy/92uAF9199YCd7vzkW4Dtfse9DkwHnijH\nsRpjTMQVlKROSUkhNTUVgKeeeorhw4fTu3dvXn31VZo3b15YVW/27NlMnjyZqKgoateuzYwZMyp1\nsmyMOcES5mpCVVe5UzSyga3AEr/djwHL3fa1OAl0gbeAJ3GSZmOMqTZClaQG+OSTT05qGzx4MIMH\nDy7rYRljIsAS5ipMVUcFfB6Ds8RcYL/JwOQQp+kMzFbV/aU+QGOMMcaYSsASZhOSiDwPXAFcGemx\nGGOMMcZEiiXMJiRVHRLpMRhjjDHGRJoVLjHGGGOC2LZtG927dycxMZGkpCQmTJgAwN69e+nRowet\nWrWiR48e7Nu3D3De/o+JiSE1NZXU1FRGjx4dyeEbY0qRJczGGGNMEFFRUYwbN47169ezbNkyJk2a\nxLp16xg7diyXXHIJGzdu5JJLLmHs2LGFx3Tp0oXs7Gyys7MZOXJkBEdvjClN5ZYwi8goERkmIqNF\n5NJTOD5dRD4si7EVc12fiJwfpD1DRCaW93hKU6h789s/RkS2iUhueY7LGGMqgri4ONq3bw9AvXr1\nSExMZPv27cyZM4f+/fsD0L9/f957771IDtMYUw7KfQ6zqlbpH7lFJEpVI1Zuq5Sv/wEwEfBc29VK\nY3tjZYy9s1h5Y3HyrqSlsQFycnJYvXo1aWlp7N69m7i4OMBJqr///vvCfkuXLqVdu3Y0adKE5557\njqSkpNK/AWNMuSvThFlERgD9gG3AHmCliEwFPlTV2W4RjZ5AHrBAVYe5+48ASUBj4H5V/TDgvJ2A\nTKA2cBi4TVU3iMgSYIiqZrv9soA7VXVNkLGFOkdt4DWgDbDe3V9wzG3Aw8BO4FvgqNs+FdgLnAes\nEpGRwPNACk6MR6nqHBFJcs9dE+fpfi9gB/A20AyoATyhqjNDxDMHmAl0d5v+oKqbSnD9kPcWjKou\nc68brpuVxj4FVsbYO4uVNxYn70pSGhvg8OHD3HPPPdx+++2sWrWKvLy8k8pn+3w+Dh48yJtvvknt\n2rVZtmwZl19+OW+++WYZ3UX5qKxljMubxcm7yhqrMkuYRaQDcDNOEhcFrAJW+u2PBa4HWquqikgD\nv8PjgW7AOcBiEWkZcPpvgK6qmudO73gKJ/l8BcgA7hWRc4FawZLlYs5xJ3BIVduKSFt33IhIHPA4\n0AH4CVgMrPY737nApaqaLyJPAYtUdYB7XytE5GNgEDBBVd8SkZo4CfKVwA5Vvcq9Tkz4yPKzqnYS\nkX44Cf/VJbj+/wS7t1/LvzR28xYtddxaW3ylOENT8rA4eWOx8sbi5F1xscq5Jb1w+9ixY1x99dUM\nGjSI+++/H4CmTZuSkJBAXFwcO3fupEmTJieV+k1PT+eFF14gOTmZhg0blsVtlIvKWsa4vFmcvKus\nsSrL765dgHdV9RCAiLwfsP9nnCfJr4jIR4D/U+S3VfU4sFFENgOtA46NAaaJSCtAgdPd9lnAYyLy\nADAAmBpmfKHO0RX4G4CqrhGRgoQ7DfCp6h73fmbiJKkFZqlqvrt9GdBTRIa5n88AmgNLgREi0gz4\nP1XdKCJrgedE5BmcJ+/+FfiCme735/gSXj/UvZWa2qfXYEOQX2eaonw+X5H/lE1oFitvLE7eeY2V\nqjJw4EASExMLk2WAnj17Mm3aNIYPH860adO49tprAdi1axeNGzdGRFixYgXHjx/nzDPPLKvbMMaU\no7J+HBG8pijgPtntBFyC8yR6MHBxiOMCPz8BLFbV60UkHvC55zwkIguBa4HeQMgX2kKdo5hxh7wf\n4KDftgC9VHVDQJ/1IrIcuAqYLyK3q+oi92n8lcDTIrJAVcOtRaQhtou9vju1Itw9GGOMcWVlZfHG\nG2+QkpJCamoqAE899RTDhw+nd+/evPrqqzRv3pxZs2YBMHv2bCZPnkxUVBS1a9dmxowZxU5pM8ZU\nDmWZMH8GTHXnKUcB1wAvFuwUkWigjqrOFZFlwCa/Y28SkWnA2UALYANwgd/+GGC7u50RcN1XcF5W\nW6Kqe8OML9Q5PgNuwZkKkgy0dduXAxNE5Eycp+M3AV+GOPd8YIiIDHGnm5ynqqtFpAWwWVX/5m63\nFZFvgL2q+qa7GkXg/QTqA4x1/1xakuuHuTdjjDEBOnfujGrwZwyffPLJSW2DBw9m8ODBZT0sY0wE\nlNmycqq6CucFtWzgHSBwqkE94EN3WsCnwH1++za4bf8EBqnqkYBjn8V5GpuFMw/Y/7orcRLa14oZ\nYqhzTAai3XE9CKxwz7sTGIWTpH5M+Pm/T+BM8VgjIl+5n8FJcr8SkWycaSav47yYt8JtGwE8Wcy4\na7lPqe+haMy8XD/ovYUiIs+KyHdAHRH5TkRGFTM2Y4wxxpgqp0ynZKjqGGBMmC6dQrRnqWqRZFBV\nfZyYerGUovOHHyvYEJEmOD8ILChmbEHPoaqHcaaIBDvmNYIk4qqaEfD5MM4LdoH9ngaeDmie7355\nNUlVHz/F64e8t2BU9UGcxNoYY4wxptqqUpX+3JUjlgMj3JcGjTHGVFADBgygUaNGJCcnF7b16dOn\nsLR0fHx84dzhY8eO0b9/f1JSUkhMTOTppwOfPRhjTNmpcGsQBT4tLeGxr+NMcyjkrp18T0DXLFW9\n61SvU9ZE5F2c+dv+HlLV+DK63nKgVkDzraq6tiyuZ4wxABkZGQwePJh+/foVts2ceWIZ+qFDhxIT\n46y0OWvWLI4ePcratWs5dOgQbdq0oW/fvsTHx5f3sI0x1VC5Jczu/NdcoD7wmap+XMLj04Fhqnp1\ncX39hZpGUYLr+tzrfhHQngGcr6ql/oaHql5f2ucMxu/e0oLsq+Mu93cOkA98oKrDy2NcxpjqoWvX\nruTk5ATdp6q8/fbbLFq0CHBW+Tl48CB5eXkcPnyYmjVrUr9+/XIcrTGmOiv3KRmqOrKkyXJlIiIR\nfWpfytd/TlVb4xSfuUhErijFcxtjTEhLliyhcePGtGrVCoAbb7yRunXrEhcXR/PmzRk2bBixsbER\nHqUxprqw0thWGjsot+DMYnf7FxFZ5Y4xrMPH8okf/lFx3aq9oSl5ZFicPLFYeVNZ4pTjsbDR9OnT\n6du3b+HnFStWUKNGDXbs2MG+ffvo0qULl156KS1atCiroRpjTCErjW2lsYvlnuMaYEKI/XcAdwA0\nbHgWI1PyvJy2Wmtc20lwTPEsVt5Uljj5fL4in3ft2sXBgweLtOfn5zNz5kxefPHFwvbMzEzatGlD\nVlYWAC1atGDatGl0796dksrNzT1pHCY4i5U3FifvKmusrDS2lcYOy53iMR34m6puDtZHVV8CXgJI\nSEjQIbdcW9xpqz2fz0fv9PRID6NSsFh5U1njlJOTQ926dUn3G/u8efNISUnhpptuKmxbvnw533zz\nDd26dePQoUNs3bqVZ555hrZtS15/yefzFbmeCc1i5Y3FybvKGquynsMctjQ2zjrM7wDXAfPCHBeq\nNHYyzpPPM9xzHgL8S2P/I8zYgp6jmHGXtDR2qvvVXFXXq+o/cKagHMYpjX2xqn6L89R6LU4hlZFh\nrhE4huJKYxe5vod7COYlYKOqZpbwOGOMCatv375ceOGFbNiwgWbNmvHqq68CMGPGjCLTMQDuuusu\ncnNzSU5OpmPHjtx2222nlCwbY8ypsNLYJ5/DSmO7RORJnDjdXsyYjDGmxKZPnx60ferUqSe1RUdH\nM2vWrDIekTHGBFdmCbOqrnKnLWQDWwleGnuOiJyB80Q0WGnsxrilsUXE/9hncaZT3A8sCrjuShHx\nWho72DkmA6+50xWy8SuN7S6NtxTnpb9VBJTl9vMEzvziNeIMPAdnrnEf4I8icgzYBYwGOgL/KyLH\ngWM4c6jDKSiNfRrQN0SfUNcPem/BuNNGRuDM9V7lxn+iqr5SzPiMMcYYY6oUK40dcA4rjV3Y9zuc\nH2SMMcYYY6o1K41tjDHGGGNMGFYauwKy0tjGmKpgwIABfPjhhzRq1IivvvoKgD59+rBhwwYA9u/f\nT4MGDcjOzmbFihXccccdgFPlb9SoUVx/fbkUPTXGmGJVuIS5tP3a0tiRUF6lsf2ud1JpbGOM+bUy\nMjIYPHgw/fr1K2ybOfNEXaahQ4cSE+MsPZ+cnMwXX3xBVFQUO3fupF27dlxzzTVERVX5/6aMMZVA\nuU3JEJFRIjJMREa7hUJKeny6iHxYfM/SJSI+ETk/SHuGiEws7/GUplD35re/g4isFZFNIvI3CXjz\n0hhjwunatWvI8tWqyttvv124fFydOnUKk+MjR45g326MMRVJuf/orqrFrTNcqYlIlLvGdFW4/mSc\nCn7LgLnAfwP/DHeAlcb2prKUMa4ILFbeVKQ4eSl/vWTJEho3bkyrVq0K25YvX86AAQPYunUrb7zx\nhj1dNsZUGGX63UhERgD9gG3AHmCliEzFqWg3212juSeQByxQ1WHu/iNAEs6ycver6ocB5+2Es2xa\nbZwiILep6gYRWQIMUdVst18WcGew8thhzlEbZwpHG2C9u7/gmNuAh3GWlfsWOOq2TwX24pQBX+UW\nH3keSMGJ8ShVnSMiSe65a+I83e8F7ADeBprhLFP3hKqe+J1l0THnADOBglqwf1DVTSW4fsh7C3Kt\nOKC+u5oIIvI6ToGZkxJmK41dcpWljHFFYLHypiLFyb/sbbDS1wDjx4+nU6dOJ7VPmjSJrVu38sgj\nj1C3bl1q1qxZ6uOrrKV5I8Fi5Y3FybvKGqsyS5hFpAPOEmbnuddZBaz02x8LXA+0dotrNPA7PB7o\nBpyDU2SjZcDpvwG6qmqeO73jKZzk8xWcwh/3isi5QK1gyXIx57gTOKSqbUWkrTvuggTycZyqfD8B\ni4HVfuc7F7hUVfNF5ClgkaoOcO9rhYh8DAwCJqjqWyJSEydBvhLYoapXudeJCR9ZflbVTu6KIJk4\n6yt7vf7/BLu3EJoC3/l9/s5tO4l/aezmLVrquLX2VKg4Q1PysDh5Y7HypiLFKeeW9BPbQUpf5+Xl\n0adPH1auXEmzZs2CnmPq1KnExsZy/vkhZ42dsspamjcSLFbeWJy8q6yxKsvvrl2Ad91y1YjI+wH7\nf8Z5kvyKiHwE+D9FfttdFm6jiGwGWgccG4NTdKQVTqnn0932WcBjIvIAMACYGmZ8oc7RFfgbgKqu\ncYt8AKQBPlXd497PTIqu4zxLVfPd7cuAniIyzP18BtAcp+jJCLcoyP+p6kYRWQs8JyLP4Dx5Dyzw\nEmi635/jS3j9UPcWTLAJhMWW1a59eg02ePh1bHXn8/mKJBUmNIuVN5UpTh9//DGtW7cukixv2bKF\n3/3ud0RFRbF161Y2bNhAfHx85AZpjDF+yvqlv5AJljvPthPwDs6v+ueFOS7w8xPAYlVNxim5fYZ7\nzkPAQuBaoDfwjzBjC3qOYsYdLmE86LctQC9VTXW/mqvqelX9B84UlMPAfBG5WFW/xXlqvRZ42p1O\nEY6G2C72+h7uwd93ONNECjTDmT5ijDGe9O3blwsvvJANGzbQrFkzXn31VQBmzJhR+LJfgX/961+0\na9eO1NRUrr/+ev7+97/TsGHDSAzbGGNOUpZPmD8DprrzlKNwktIXC3aKSDRQR1XnisgyYJPfsTeJ\nyDSctYhb4JTKvsBvfwyw3d3OCLjuK8AHwBJV3RtmfKHO8RlwC85UkGSgrdu+HJggImfiPB2/Cfgy\nxLnnA0NEZIg73eQ8VV0tIi2Azar6N3e7rYh8A+xV1TdFJDfI/QTqA4x1/1xakuuHubeTuKXAD4jI\nBe6998OZF22MMZ5Mnz49aPvUqVNParv11lu59dZby3hExhhzasosYVbVVe60hWxgKxA41aAeMEdE\nzsB5IupfCnsD8CnOS3+DVPVIwBJDz+JMp7gfWBRw3ZUi8jPFr70c6hyTgdfc6QrZwAr3vDtFZBRO\nkroTZ/5vjRDnfgJnfvEadym2HJy5xn2AP4rIMWAXMBroCPyviBwHjuHMoQ6nllto5DSgb4g+oa4f\n9N7CuBNnWkttnJf9wq6QYYwxxhhTFZXpGyKqOgYYE6ZLpxDtWarqn0Cjqj7A524vpej84ccKNkSk\nCU4yuaCYsQU9h6oexnlZMdgxQYugBFYndM/xP0H6PQ08HdA83/3yapKqPn6K1w95b8Go6hdAcgnG\nZowxxhhT5ZRb4ZLy4K4csRwY4b40aIwx5lcaMGAAjRo1Ijm56M/Pzz//PAkJCSQlJfHggw8CsGLF\nClJTU0lNTaVdu3a8++67kRiyMcaUqoqxBpGfwKelJTz2deB1/zZ37eR7Arpmqepdp3qdsiYi7+LM\n3/b3kKrGh+g/Htiqqpnu5/nANlW93f08Dtiuqn/1O2YqJ9bDXg7UCjjtraq6tjTuxxhTuQUrcb14\n8WLmzJnDmjVrqFWrFt9//z1gJa6NMVVTlf8OFmoaRUWmqteX8JB/47yEmCkipwENgfp++38P3Bvm\nemklHqQxptro2rUrOTk5RdomT57M8OHDqVXL+Vm7UaNGgFPiuoCVuDbGVBVVakpGNZaFkxSDUyHx\nK+CAiPxGRGoBiUC2iEwUkXXuuteNCg4WkZEi8rmIfCUiL4njHBFZ5denlYisxBhjgG+//ZYlS5aQ\nlpZGt27d+Pzzzwv3LV++nKSkJFJSUnjhhRfs6bIxptKz72JVgKruEJE8EWmOkzgvxanKdyFOVcI1\nwFVAAk657MbAOmCKe4qJqjoaQETeAK5W1Q9E5CcRSXVLjd9G+EIwABw+lk/88I9K9f6qoqEpeWRY\nnDyxWHlT2nHKKaYAUV5eHvv27WPZsmV8/vnn9O7dm82bNyMipKWl8fXXX7N+/Xr69+/PFVdcwRln\nnBH2fMYYU5FZwlx1FDxl/j3wV5yE+fc4CfO/car8TXerAe4QEf+l9LqLyINAHSAW+BpnLetXgNvc\npff6EGJVExG5A7gDoGHDsxiZklf6d1fFNK7tJDimeBYrb0o7Tj6fr8jnXbt2cfDgwcL2OnXq0KJF\nCz799FMAfvnlF+bMmUODBg2KHHfs2DGmTZtGQkJCqY3t18rNzT3p/kxwFitvLE7eVdZYWcJcdfwb\nJ0FOwZmSsQ0YilNkZQpwCUGq/LnrYP8dOF9Vt7lrTRc8CnoH+AvOOtUrVfXHYBdW1ZeAlwASEhJ0\nyC3Xlt5dVVE+n4/e6emRHkalYLHypqzjlJOTQ926dUl3rzFgwAB27NhBeno63377LaeddhrXXnst\nOTk5RUpc7969m169elWoqn0+n6/wPkx4FitvLE7eVdZY2RzmqiMLpzjJXlXNd6scNsCZlrEUp8rf\nzSJSQ0TigO7ucQXJ8Q9u9cUbC06oqkdw1oieTCV7cdIYU3qClbgeMGAAmzdvJjk5mZtvvplp06Yh\nIlbi2hhTJdkT5qpjLc7qGP8IaItW1R/cpeoudtu+xamkiKruF5GX3fYc4HOKegu4gWIKwRhjqq5Q\nJa7ffPPNk9qsxLUxpiqyhLmKcOcm1w9oy/DbVmBwiGMfBR4NcerOwBT3/MYYY4wx1Y4lzCYk96n0\nOThPpo0xxhhjqiVLmE1Ip1BAxRhjjDGmyrGX/owxppoZMGAAjRo1Ijk5ubBt1KhRNG3alNTUVFJT\nU5k7dy7grI5Ru3btwvZBgwZFatjGGBMx9oTZFCEic4E/qOr+gPZRQK6qPheRgRljSk1GRgaDBw+m\nX79+Rdrvu+8+hg0bdlL/c845h+zs7PIanjHGVDj2hLmaccteh/x7V9UrA5NlY0zV0rVrV2JjYyM9\nDGOMqTTsCXMlICL9gGE4hUfWAPcDLwDN3S73qmqW+xS4OdDC/TNTVf8mIvHAP4HFOOsyXycivwce\nAQT4SFUfcq+Vg1PE5AcRGQH0wymCsgdYWdxYrTS2N1bu2TuLlTfFxam4UtcAEydO5PXXX+f8889n\n3Lhx/OY3vwFgy5YtnHfeedSvX58nn3ySLl26lNq4jTGmMhBntTFTUYlIEvB/wEVuEhsLTAT+rqr/\nEpHmwHxVTXQT5stwipLUAzYAv8Upk70Z+L2qLhORJsAyoAOwD2eN5b+p6nsFCTPwX8BUIA3nB6tV\nwAvBpmQElMbuMDLz5TKJRVXSuDbsPhzpUVQOFitviotTStOYIp937drFww8/zGuvOTWJ9u7dS0xM\nDCLClClT+PHHH3nooYf45ZdfOHz4MDExMWzYsIHHHnuM1157jbp165bl7ZSp3NxcoqOjIz2MSsFi\n5Y3FybuKFqvu3buvVNXzi+tnT5grvouB2ar6A4Cq7hWRS4E2IlLQp76I1HO3P1LVo8BREfkeaOy2\nb1XVZe52R8CnqnsAROQtoCvwnt91uwDvquoht8/7oQZopbFLzso9e2ex8qakcQosde2vRYsWXH31\n1SftS09PZ/r06TRu3Jjzzy/2/5cKq7KW5o0Ei5U3FifvKmusLGGu+ARnKoa/04ALVbXI8yQ3gT7q\n15TPib/jgwHn9MJ+/WBMNbFz507i4uIAePfddwtX0NizZw+xsbHUqFGDzZs3s3HjRlq0aBHJoRpj\nTLmzl/4qvk+A3iJyJoA7JWMBflX7RCS1hOdcDnQTkYYiUgPoi1sq289nwPUiUtt9en3Nqd6AMaZi\n6du3LxdeeCEbNmygWbNmvPrqqzz44IOkpKTQtm1bFi9ezPjx4wH47LPPaNu2Le3atePGG2/khRde\nsBcGjTHVjj1hruBU9WsRGQN8KiL5wGrgbmCSiKzB+Tv8DPC8OKqq7hSRh3FeAhRgrqrOCeizSkRm\nAtnAVmBJqdyQMSbipk+fflLbwIEDg/bt1asXvXr1KushGWNMhWYJcyWgqtOAaQHNfYL0GxXwOdnv\nY3LAvn8A/whyjni/7THAmBIP2BhjjDGmCrEpGcYYY4wxxoRhCbMxJiImTJhAcnIySUlJZGZmAjDr\n/7N373FWlWX/xz9fRUEEQQUTLUVJETkNgaKJOFR4JJUwzLAHT5kntMwDZh7ysTRBBQ9pYgiPqRme\nwENqCiNKKIICmor6Uwx9fETwACMow3D9/lhrcDPMnlkoM3sO3/frNa+99r3WXuvaF2TXLO51X5Mm\n0bVrVzbZZBNmz55d4AjNzMwSLpjrmKSOkl6u68+a1Scvv/wy48aNY9asWcybN4+HHnqIN954g27d\nunHffffRv3//QodoZma2lgvmRkCS56Jbg/Lqq6+yzz770LJlS5o1a8YBBxzA/fffT5cuXejcuXOh\nwzMzM1uHC+bCaCZpoqT5ku6R1FJSb0lPSZoj6TFJHQDS8XmSZgKnV5xA0nGSJkl6EHhciVGSXpb0\nkqSj0+PyjRen1/u7pNclXSlpmKRZ6XGd0uN+nH52nqTpdZ8qa4y6devG9OnTWbp0KStWrOCRRx5h\n0aJFhQ7LzMysSr4zWRidgRMjYoak8SSF8GDgiIj4MC1qfw+cANwGjIiIpySNqnSefYEeafe/IUAR\n0BNoBzyfFrjfzTNOOtYF+IikdfatEbG3pLOAEcAvgYuBgyLiPUlta/piK8vK6Tjy4a+alybj191X\nc1wTy9PCKw9bu92lS/hVI1AAACAASURBVBfOP/98Bg4cSKtWrejZsyfNmvk/R2ZmVj/5/6EKY1FE\nzEi3/wr8hmTZt3+m3fo2Bd6X1AZoGxEVTUVuBw7JOc8/I+KjdLsfcFdElAMfSHqKpAV2vvFlwPMR\n8T6ApP9H0hAF4CVgQLo9A5gg6e/AfVV9GUknAycDtGvXnou7r/4qOWlSvrFFUjQ3JSUlJeu879Sp\nE9dccw0A48aNo0WLFmuP+eSTT5gzZw6lpaWUlpau91lbn/OUnXOVnXOVjfOUXUPNlQvmwqjccno5\n8O+I2Dd3ML2jW1176iztrqtrg53bRntNzvs1pH83IuIUSX2Bw4C5kooiYmnuSSLiFuAWgM6dO8eI\nYUdUc0mDpHgcWlxc6DAKavHixWy33Xb85z//Yc6cOcycOZOtt94agLZt29K7d2/69OlDSUkJxU08\nV1k4T9k5V9k5V9k4T9k11Fx5DnNh7CSpojg+BngWaF8xJmkzSV0j4hPgU0n90mOHVXPO6cDRkjaV\n1B7oD8yqZjwTSZ0i4rmIuBhYAnxrA76nWV5Dhgxhzz335Ic//CE33ngjW2+9Nffffz/f/OY3mTlz\nJocddhgHHXRQocM0MzPzHeYCeRUYLunPwBvA9cBjwHXpNIxmwBjg38DxwHhJK9Jj8rmfZE7zPJK7\n0udFxP9Jyje+R8ZYR0najeRO9ZPpecy+tqefXr/b+uDBgxk8ePA6Yw3xn+7MzKxxccFcxyJiIbBn\nFbvmktz9rXz8HJKH8ypcmo5PACbkHBfAuekPGcZLgJKc98VV7YuIH1X7hczMzMwaOU/JMDMzMzOr\nhu8wmzVxCxYs4Oijj177/q233uKyyy7jvffe48EHH2TzzTenU6dO3HbbbbRtW+PKgmZmZo2O7zCb\nNXGdO3dm7ty5zJ07lzlz5tCyZUsGDx7MwIEDefnll5k/fz677747V1xxRaFDNTMzKwgXzE2cpBJJ\nfdLtRyqak0g6U9Krku6Q1FzSE5LmVnQKtMbpySefpFOnTuy8884ceOCBa5uJ7LPPPrz77rsFjs7M\nzKwwPCWjHlDSrUQRsaaQcUTEoTlvTwMOiYi3Je0DbBYRRQUKzerI3/72N4455pj1xsePH7/OtA0z\nM7OmxAVzgUjqCPwDmEay7NsYSeeQLN/2cEScnx53DEknwMrjpcCNwA+Aj9NjrgJ2An4ZEVPyXHcL\nknbbe5Isb7dFzr6FQB/gcmBXYIqkvwI/J1knei4wJCL+X77v5dbY2dSH1ti5raoBVq1axZQpU9ab\nevH73/+eZs2aMWxYdcuAm5mZNV5KVh2zupYWzG8B3wX+Q9K8pDdJ8fs4cB1Jg5H1xiPiAUkBHBoR\n/0jXWt6SpBvfnsDEfHeDJZ0NdIuIEyT1AF4A9omI2RUFc0QsqbRdDJwTEYPynDO3NXbvi8eM+1q5\naQq+sQV8sLKwMXTfsc0675955hkmT57MqFGj1o49+uijPPjgg1x99dW0aNGirkMEkjaqrVq1Ksi1\nGxLnKTvnKjvnKhvnKbv6lqsBAwbMiYg+NR3nO8yF9U5EPCvpCKAkIj4EkHQHyZrMkWf8AWAV8Gh6\nnpeALyKiTNJLQMdqrtmfpBgnIuZLmv91v4RbY2+4+tga++abb+a0005b27L00UcfZcqUKTz11FO0\nb9++YHE11Daqdc15ys65ys65ysZ5yq6h5soP/RXWZ+mr8uzPNw5QFl/+88Aa4AuAdB50Tb8I+Z8V\nbB0rVqzgn//8Jz/60Zd9as444wyWL1/OwIEDKSoq4pRTTilghGZmZoXjO8z1w3PAWEntSKZeHEPS\nLntWnvGvYzowDJgmqRvQ42uezxqBli1bsnTp0nXG3nzzzQJFY2ZmVr+4YK4HIuJ9SReQPAAo4JGI\nmAyQb/xruAm4LZ2KMZekKDczMzOzPFwwF0hELAS65by/E7iziuPyjbfK2b40374qPrcS+EmefR3z\nbJcAJfnOaWZmZtaYeQ6zmZmZmVk1XDA3UpIOSjvz5f7cX+i4rO598sknHHXUUeyxxx506dKFmTNn\nAnD99dfTuXNnunbtynnnnVfgKM3MzOovT8logCqtkVxa1RSMiHgMeGwjXvO49JpnbKxzWt0466yz\nOPjgg7nnnntYtWoVK1asYNq0aUyePJn58+fTvHlzFi9eXOgwzczM6i0XzGaN2LJly5g+fToTJkwA\nYPPNN2fzzTfnpptuYuTIkTRv3hyA7bbbroBRmpmZ1W8umOs5SQ8A3wJaAGPTJiFVHSeS1tiHkKyz\nfHlE3C3pT8CjETElnZLxcdrl70Rgl4j4raRjgTOBzUmWuDstIsolHQ9cALwPvE661nN13Bo7m9pu\njV3R9vqtt96iffv2HH/88cybN4/evXszduxYXn/9dZ5++mkuvPBCWrRowejRo9lrr71qLR4zM7OG\nzHOY678TIqI30Ac4U9K2eY77EVAE9AR+AIyS1IFk3eX902N2JGmdDdAPeFpSF+BoYL+0nXY5MCz9\n7O+A/YCBOZ+zBmT16tW88MILnHrqqbz44otsueWWXHnllaxevZqPP/6YZ599llGjRjF06FC+7INj\nZmZmuXyHuf47U9LgdPtbwG55jusH3BUR5cAHkp4C9gKeBn4paU/gFWDrtBjel+Su8nCgN/B8cpOa\nLYDFQF/Wbct9N7B7VReWdDJwMkC7du25uPvqr/eNm4BvbJHcZa4tJSUlAHz00Ue0a9eOlStXUlJS\nQqdOnbjzzjtp2bIlu+66K0899RQAq1atYvLkybRt27bWYvqqSktL134fy895ys65ys65ysZ5yq6h\n5soFcz0mqZjkbvG+EbFCUgnJ1IwqD69qMCLek7Q1cDDJ3eZtgKFAaUQsT6dyTIyICypd+0gyttBO\np4ncAtC5c+cYMeyILB9r0kpKShhaXFwn17r22mvp0KEDnTt3pqSkhP33359OnTrxv//7vxQXF/P6\n66+zySabcMQRR5D+0lSvlJSUUFxHuWrInKfsnKvsnKtsnKfsGmquXDDXb21I5hyvkLQHsE81x04H\nfiFpIklR3B84N903E/gl8D1gW+Ce9AfgSWCypGsjYrGkbYDWfNmue1tgGfBjYN5G/XZWJ66//nqG\nDRvGqlWr2HXXXbntttvYcsstOeGEE+jWrRubb745EydOrJfFspmZWX3ggrl+exQ4JW1jvQB4tppj\n7yeZZjGP5M7weRHxf+m+p4EDI+JNSe+QFNRPA0TEK5J+CzwuaROgDDg9Ip6VdClJsf0+8AKw6cb+\nglb7ioqKmD179nrjf/3rXwsQjZmZWcPjgrkei4gvSFa9qKxjzjGt0tcguaN8buWDI+IvwF/S7TJg\ny0r77wburuJztwG3feUvYGZmZtYIeJUMMzMzM7NquGA2a8Cqanv90UcfMXDgQHbbbTcGDhzIxx9/\nXOgwzczMGjQXzE2UpDMlvSrpjkLHYl9dRdvr1157jXnz5tGlSxeuvPJKvv/97/PGG2/w/e9/nyuv\nvLLQYZqZmTVoLpibrtOAQyNiWKEDsa+mou31iSeeCCRtr9u2bcvkyZMZPnw4AMOHD+eBBx4oZJhm\nZmYNngvmJkjSzcCuwBRJF0oaL+l5SS9KOiI9ZlNJo9Lx+ZJ+UdiorbLctte9evXipJNO4rPPPuOD\nDz6gQ4cOAHTo0IHFixcXOFIzM7OGzatkNEERcYqkg4EBwNnA1Ig4QVJbYJakJ4BhwKcRsZek5sAM\nSY9HxNvVnXtlWTkdRz5c69+hoft199Uc9xXztPDKw4Av215ff/319O3bl7POOsvTL8zMzGqBC2Y7\nEDhc0jnp+xbATul4D0lHpeNtSNpyr1cwuzX2hvs6rbFranu91VZbce+997LtttuydOlSWrdu3SDb\nkFZoqG1U65rzlJ1zlZ1zlY3zlF1DzZULZhMwJCIWrDOYtH0bERGP1XQCt8becBurNXZVba8B3njj\nDYYMGcKVV17JT37ykwbZhrRCQ22jWtecp+ycq+ycq2ycp+waaq5cMNtjwAhJIyIiJPWKiBfT8VMl\nTY2IMkm7A+9FxGeFDddyVdX2es2aNQwdOpS//OUv7LTTTkyaNKnQYZqZmTVoLpjtv4ExwPz0rvJC\nYBBwK0lHwRfS8Q+BIwsUo+WRr+31k08+WYBozMzMGicXzE1URHTMebveChgRsQb4TfpjZmZm1mR5\nWTkzMzMzs2q4YDYzMzMzq4YLZrN6rGPHjnTv3p2ioiL69Omzzr7Ro0cjiSVLlhQoOjMzs6bBBXM9\nJKmjpJc34PjDJY1Mty/NWVO5ynNK6iPpuo0XsdWmadOmMXfu3HUe7lu0aBH//Oc/2WmnnQoYmZmZ\nWdPggrkRiIgpEZG5xVtEzI6IM2szJqtdv/rVr7jqqqtIFjAxMzOz2uRVMuqvZpImAr2A14H/Al4B\n+kTEEkl9gNERUSzpuHT8jNwTSOoNjAdWAM/kjBcD50TEIEmXknT22zV9HRMR16XHXUTSInsRsASY\nExGjqwvarbGzydcau6LtdQVJHHjggUjiF7/4BSeffDJTpkxhxx13pGfPnnUVrpmZWZPmgrn+6gyc\nGBEzJI0HTvsK57iNpFvfU5JGVXPcHsAAoDWwQNJNQE9gCEnB3gx4AZhT1YfdGnvD5WuNXbld6KhR\no2jXrh0ff/wx55xzDitXruTmm29m1KhRlJSU8PnnnzNjxgzatGlTR5HXvYbaRrWuOU/ZOVfZOVfZ\nOE/ZNdRcuWCuvxZFxIx0+6/ABk2hkNQGaBsRT6VDtwOH5Dn84Yj4AvhC0mLgG0A/YHJErEzP92C+\na+W2xt5p12/H1S/5r1VNft19NVXlaeGw4ryfmTdvHsuWLWPp0qWccUbyjwlLlixhxIgRzJo1i+23\n3762wi2ohtpGta45T9k5V9k5V9k4T9k11Fy5sqm/oor3q/ly3nmLGj6vKs6Rzxc52+Ukfy++0uTY\nLTbblAWVphXY+kpKSqotjgE+++wz1qxZQ+vWrfnss894/PHHufjii1m8ePHaYzp27Mjs2bNp165d\nLUdsZmbWdPmhv/prJ0n7ptvHkMxBXgj0TseGVPfhiPgE+FRSv3Ro2AZe/xngh5JaSGoFuAquYx98\n8AH9+vWjZ8+e7L333hx22GEcfPDBhQ7LzMysydngO8yStga+FRHzayEe+9KrwHBJfwbeAG4CZgF/\nkfQb4LkM5zgeGC9pBfDYhlw8Ip6XNAWYB7wDzAY+3ZBz2Nez6667Mm/evGqPWbhwYd0EY2Zm1oRl\nKpgllQCHp8fPBT6U9FREnF2LsTVZEbEQ2LOKXU8Du1dx/ARgQrp9ac74HJKH9ypcmo6XACWVj0/f\nd8t5OzoiLpXUEpgOXL0h38PMzMysMcg6JaNNRCwDfgTcFhG9gR/UXlhWT9wiaS7JChn3RsQLhQ7I\nzMzMrK5lLZibSeoADAUeqsV4rB6JiJ9GRFFE7BERVxQ6nsamvLycXr16MWjQIACefPJJvvOd71BU\nVES/fv148803CxyhmZmZQfaC+TKSObD/L53buivJvFqrZZJK6+AaJWkjFKtDY8eOpUuXLmvfn3rq\nqdxxxx3MnTuXn/70p1x++eUFjM7MzMwqZCqYI2JSRPSIiFPT929FRLWrNFjDJ2nTQsfQWH344Yc8\n/PDDnHTSSWvHJLFs2TIAPv30U3bYYYdChWdmZmY5sj70tzvJKg3fiIhuknoAh0eEb4HVkXRpt8nA\n1sBmwG8jYrKkjsBDFQ/rSToHaJU+rFdCsprGAKAtSefApyVtQdIFcE+S1Ti2yLlOKXANcBDwiKSi\niBic7hsInBoRP6qDr9yo3XDDDVxzzTUsX7587ditt97KoYceyhZbbMFWW23Fs88+W8AIzczMrELW\nZeXGAecCfwaIiPmS7gRcMNedz4HBEbFMUjvg2XTZt5o0i4i9JR0KXELysOapwIqI6JH+8pP7MN+W\nwMsRcbEkAa9Kah8RH5IsU3dbdRdbWVZOx5EPf4Wv1/gtTBu6PPTQQ7Rt25bevXuv0x702muv5ZFH\nHqFv376MGjWKs88+m1tvvbVA0ZqZmVmFrAVzy4iYldRPa62uhXgsPwF/kNQfWAPsSNLCuib3pa9z\ngI7pdn/gOlj7y0/umtrlwL3pvpB0O3CspNuAfYH/Wi8w6WTgZIB27dpzcXf/1ahKRXF81113MWPG\nDLbffntWrVrFihUr2GeffVi0aBErV66kpKSEnXbaiRtvvHGdgrqpKi0tdR4ycJ6yc66yc66ycZ6y\na6i5ylowL5HUibTVsqSjgPdrLSqryjCgPdA7IsokLSRpj53bLhvWb5ld0fa6ouV1hXxtsz+PiPKc\n97cBD5Lc4Z4UEetVwxFxC3ALQOfOnWPEsCMyfaGmqri4mJKSkrWvo0eP5oEHHmD77bdnhx12YPfd\nd+cvf/kLvXv3pri4uNDhFlxFrqx6zlN2zlV2zlU2zlN2DTVXWQvm00kKoj0kvQe8zYa3Wravpw2w\nOC2WBwA7p+MfANtJ2hYoBQYBj9Zwrukkf37TJHUDeuQ7MCL+V9L/Ar8FBn7N72B5NGvWjHHjxjFk\nyBA22WQTtt56a8aPH1/osMzMzIwMBbOkTYA+EfEDSVsCm0TE8po+ZxvdHcCDkmaTdFt8DSAtoC8j\nebjv7YrxGtwE3JZOxZhL0nK7pmu3j4hXvmrwVrXi4uK1v2kPHjyYwYMHFzYgMzMzW0+NBXNErJF0\nBvD3iPisDmKyHBHRKn1dQjKHuKpjriOdk1xpvDhnewnpHOaIWAn8pLrrVdKP5MFPMzMzsyYna+OS\nf0o6R9K3JG1T8VOrkVm9IGkOyZSNvxY6FjMzM7NCyDqH+YT09fScsQB23bjhWH0TEb0LHYOZmZlZ\nIWXt9LdLFT8uls0yKC8vp1evXgwaNGid8REjRtCqVVUzYMzMzKw+ydrpb721dwEi4n82bjj2VUk6\nk6QhyVbA/RFxRoFDstTYsWPp0qXL2rbXAAsWLOCTTz4pYFRmZmaWVdY5zHvl/OwPXAocXksx2Vdz\nGnAocOHGOJmkrNN1rBrvvvsuDz/8MCeddNLasfLycm6++WauuuqqAkZmZmZmWWUqiiJiRO57SW2A\n22slIttgkm4mmU8+BRifM75z+r498CFwfET8p5rxCcBHQC/ghbT19tj0dAH0r2lJQbfG/rIFNsAv\nf/lLrrrqKpYv/zJtN9xwA9/97nfp0KFDIcIzMzOzDfRV7yKuAHbbmIHYVxcRp0g6GBhA0rikwg3A\n/0TEREknkCw9d2Q14wC7Az+IiHJJDwKnR8QMSa1Iuv2tx62x11XR8nPmzJmUlZWxfPly5s6dy9Kl\nS7nnnnu49dZbufzyyykpKaG8vLxBtgitSw21jWpdc56yc66yc66ycZ6ya6i5UkS+Dsk5ByWFU8WB\nmwB7krRJPr8WY7MNkLbK7kNSMPeJiDMkLQE6pM1NNgPej4h21YxPAKZFxMT0nCOBwSSNS+6LiHdr\nimOnXb8dmwwdW9NhjVrFHeYLLriA22+/nWbNmvH555+zbNkymjdvTvPmzQFo0aIF//nPf9h11115\n8803CxlyvdZQ26jWNecpO+cqO+cqG+cpu/qWK0lzIqJPTcdlvcM8Omd7NfBOluLJ6p18vx3ljq9t\nThMRV0p6mGRu9LOSfhAR1XYS3GKzTVmQMyWhKbviiiu44oorgOQ/EKNHj+ahhx5a+764uJhWrVq5\nWDYzM6vnsj70d2hEPJX+zIiIdyX9sVYjs43hX3zZ0W8Y8EwN4+uQ1CkiXoqIPwKzgT1qMVYzMzOz\neilrwTywirFDNmYgVivOBI6XNB/4GXBWDeOV/VLSy5LmASuBf9R2wI1VcXHx2rvLuUpLSwsQjZmZ\nmW2IaqdkSDqVZLmyXdPiqkJrYEZtBmYbJiI6ppsT0h8iYiHwvSqOzTd+XKX3IyofY2ZmZtbU1DSH\n+U6Su4pXACNzxpdHxEe1FpWZmZmZWT1R7ZSMiPg0IhZGxDER8Q7JP8sH0ErSTnUSoVkDU7kV9g03\n3MC3v/1tJLFkyZICR2dmZmYbKtMcZkk/lPQG8DbwFLAQz2fNRNKlks6RdJmkH1Rz3ARJR9VBPCWS\nalw+xb66ilbYFfbbbz+eeOIJdt555wJGZWZmZl9V1of+Lgf2AV6PiF2A7+M5zBskIi6OiCcKHcfX\nIWnTQsdQ31XVCrtXr1507NixcEGZmZnZ15K1YC6LiKXAJpI2iYhpQFEtxtWgSbpQ0gJJTwCd07G1\nd5AlXSnpFUnzJeWucd1f0r8kvZVz7J8kHZ5u3y9pfLp9oqTL0+0HJM2R9O+06x6SNk2v+bKklyT9\nKuc6P5Y0S9LrkvbPOX6UpOfTuH6RjhdLmibpTuCl2sxbY1DRCnuTTbL+T8vMzMzqu6yNSz5JWyM/\nDdwhaTFJAxOrRFJvkjWOe5Hk9wVgTs7+bUi65+0RESGpbc7HOwD9SNY7ngLcA0wH9k/f75geQ3rc\n39LtEyLiI0lbAM9LuhfoCOwYEd3S6+Zep1lE7C3pUOAS4AfAicCnEbGXpObADEmPp8fvDXSLiLdr\n+v4ry8rpOPLhmg5rVCo6+z300ENst9129O7du0G2/TQzM7OqZS2YjyB54O+XJI0u2gCX1VZQDdz+\nwP0RsQJA0pRK+5cBnwO3pl30chfnfSAi1gCvSPpGOvY0yXrIewKvAFtL6gDsS7KeMsCZkgan298C\ndgMWkCwHeD3wMFBR/ALcl77OISmsAQ4EeuTMo26TnmcVMKu6Yjm9q30yQLt27bm4e9P6XaqiOL7r\nrrt4/PHHue+++1i1ahUrVqxg4MCBXHjhhQB8/vnnzJgxgzZt2lBaWuqiOiPnKhvnKTvnKjvnKhvn\nKbuGmqtMBXNEfCZpZ2C3iJgoqSXg+az55WtBTUSslrQ3yTzwnwBn8OWayF/kHKr0+PckbQ0cTHK3\neRtgKFAaEcslFZPcId43IlZIKgFaRMTHknoCBwGnp585odJ1yvny74CAERHxWG686fk/oxoRcQtw\nC0Dnzp1jxLAjqju80SouLl67XbkVNkCLFi3Yb7/9aNeu3drW2FYz5yob5yk75yo75yob5ym7hpqr\nrKtk/JxkesCf06EdgQdqK6gGbjowWNIWkloDP8zdmU5taRMRj5Dcsc8yF3xmeux0kjvO56SvkNwJ\n/jgtlvcgeTgTSe2ATSLiXuAi4Ds1XOMx4FRJm6Wf313Slhlisxpcd911fPOb3+Tdd9+lR48e6zwQ\naGZmZvVf1ikZp5PMY30OICLekLRdrUXVgEXEC5LuBuYC7/BlYVuhNTBZUguSu7q/omZPAwdGxJuS\n3iG5y1xx3keBU9JOjAuAZ9PxHYHbJFX8UnRBDde4lWR6xguSBHwIHJkhNqtCcXHx2t+gzzzzTM48\n88x19jfEf44yMzNrqrIWzF9ExKqkjgJJzahm2kFTFxG/B35fzSF7V/GZ4yq9b5Wz/RfgL+l2GbBl\nzr4vgEPyXGe9u8oRUZyzvYR0DnM6d/o36U+ukvTHzMzMrEnKuvbVU5J+A2whaSAwCXiw9sIyMzMz\nM6sfshbMI0n+if4l4BfAI8BvaysoMzMzM7P6otopGZJ2ioj/pP9cPy79MWvSPv/8c/r3788XX3zB\n6tWrOeqoo/jd737H/vvvz/LlywFYvHgxe++9Nw884GdjzczMGrqa5jA/QDoPVtK9ETGk9kOyqkha\nCPRJ5x1vrHP+JiL+sLHO11Q0b96cqVOn0qpVK8rKyujXrx+HHHIITz/95fOdQ4YM4YgjmubyemZm\nZo1NTVMylLO9a20GYgVR+QG/ainR5Hs+S6JVq+SZzLKyMsrKyqh4IBZg+fLlTJ06lSOP9CIjZmZm\njUFNd5gjz7bVIknHknTx25xkKb/TMuw/GdglIs5LjzkO6B0RIyQ9QNIBsAUwNiJukXQlyUOcc4F/\nR8QwSWfzZXOTWyNijKSOwD+AaSTdBY8kWS6vSo25NXZFC2yA8vJyevfuzZtvvsnpp59O37591+67\n//77+f73v89WW21ViDDNzMxsI1NE/jpYUjlJlzcBWwArKnYBERGuCDYySV2Aq4AfRUSZpD+RrK18\nGdAHaJ9n/z+AmRHx7fQ8/wB+HxHPSNomIj6StAXwPHBARCyVVFqxfJ2k3sAEksYnIinEjwU+Bt4C\nvhsRFWs8V445tzV274vHNM6p7t13bLPeWGlpKRdddBFnnnkmu+yyCwDnn38+hx56KAcccEDec5WW\nlq69S23Vc66ycZ6yc66yc66ycZ6yq2+5GjBgwJyI6FPTcdXeYY4It7+ue98HegPPp//MvwWwuKb9\nEfGhpLck7QO8AXQGZqSfOVPS4HT7W8BuwNJK1+0H3B8RnwFIug/YH5gCvJOvWAa3xp4zZw5Lly7l\n+OOPZ+nSpbz55pucf/75tGjRIu9nGmpr0EJwrrJxnrJzrrJzrrJxnrJrqLnK2rjE6o6AiRGxTme+\ndIpF3v2pu4GhwGskxW9IKgZ+AOybts8uIZmaUdV18/lsg75BI/fhhx+y2Wab0bZtW1auXMkTTzzB\n+eefD8CkSZMYNGhQtcWymZmZNSxN/gGueuhJ4KiK1uOStpG0c8b995HMMT6GpHgGaAN8nBbLe5BM\nuahQJmmzdHs6cKSklpK2BAazfltvA95//30GDBhAjx492GuvvRg4cCCDBg0C4G9/+xvHHHNMgSM0\nMzOzjcl3mOuZiHhF0m+Bx9MVKcqA0zPsfyciPpb0CrBnRMxKP/IocIqk+cACkvnOFW4B5kt6IX3o\nbwJQ8blbI+LF9KE/y9GjRw9efPHFKveVlJTUbTBmZmZW61ww10MRcTdf3iGu0LGG/RX7BlV6/wVw\nSJ5jzwfOz3l/DXBNpWMWAt0yB29mZmbWyHhKhpmZmZlZNXyH2WwDuTW2mZlZ0+KC2WwDuTW2mZlZ\n09IkpmRIaivptJqP3KBz/l7SIkmllcabS7pb0puSnvNDc42PW2ObmZk1LU2iYAbaUqm99EbwILB3\nFeMnkizj9m3gIy8E7wAAIABJREFUWuCPG/m6BSPJ/yKRKi8vp6ioiO22246BAwe6NbaZmVkj1lQK\noCuBTpLmAv9Mxw4BArg8Iu5OG3xcRtIBrzPJusSnRcSaqk5Y0fku985i6gjg0nT7HuAGSYoqepCn\nzUiOBDYlWYniamBz4GfAF8ChaUvrn5O0nt4ceBP4Wbqu8gRgGUnL7O2B8yLiHkmtgMnA1sBmwG8j\nYnJ6zYuAYcAiYAkwJyJGS+oE3EjSensF8POIeC29xkdAL+AF4NdVpzixsqycjiMfru6QBmvhlYet\n3d50002ZO3cun3zyCYMHD+bll1+mW7dkMZG77rqLk046qVBhmpmZ2UamKuq4RiedFvFQRHSTNAQ4\nBTgYaAc8D/QlKZIfBfYE3km3/xwR99Rw7tKIaJXz/mXg4Ih4N33//4C+EbGkis8eB/yWpBhtQVIM\nnx8RN0u6lmRt5TGSto2IpelnLgc+iIjr02J2S+BoYA9gSkR8O70T3DIilklqR7L28m4kLbVvBfYl\n+WXphfQ7jpb0JHBKRLwhqS9wRUR8L71GO+CIiCjPk4OTSQp62rVr3/viMeOqS1mD1X3HNlWOT5w4\nkRYtWnD00Ufz6aef8l//9V9MmjSJzTffPO+5SktL107rsOo5V9k4T9k5V9k5V9k4T9nVt1wNGDBg\nTkT0qem4pnKHOVc/4K60+PtA0lPAXiR3amdFxFsAku5Kj622YK5CVS2mq/utZFpELAeWS/qUZKoH\nwEtAj3S7W1ootwVaAY/lfP6B9C74K5K+kRPDHyT1B9YAOwLfSL/P5IhYmX7HB9PXVsB3gUk5d8yb\n51xjUr5iGSAibiFpgkLnzp1jxLDG/bBb5dbYF110Eeeffz7FxcXcfPPNHHnkkRx44IHVnqOkpITi\n4uK6CbiBc66ycZ6yc66yc66ycZ6ya6i5aooFc1UFbYXKhe1Xuf3+LvAt4N30Tm8bkikN+XyRs70m\n5/0avvzzmQAcGRHz0rvSxXk+X/HdhpFMregdEWWSFpLcwc733TcBPomIojz7P6sm/ibn/fffZ/jw\n4ZSXl7NmzRqGDh26TmvskSNHFjhCMzMz25iaSsG8HGidbk8HfiFpIrAN0B84l2RKw96SdiGZknE0\n6V3TDTQFGA7MBI4CplY1f3kDtQbel7QZSTH8Xg3HtwEWp8XyAGDndPwZ4M+SriD5sz8MGJdO3Xhb\n0o8jYpKS28w9ImLe14y7UXJrbDMzs6alSaySkc7/nZHOL94XmA/MA6aSPCj3f+mhM0keEHwZeBu4\nP985JV0l6V2gpaR3JV2a7voLsK2kN4GzgY1xu/Ei4DmSBxZfy3D8HUAfSbNJCuzXACLieZKCfh5w\nHzAb+DT9zDDgREnzgH+TPLxoZmZm1uQ1lTvMRMRPKw2dW8VhKyLi6IznOw84r4rxz4EfZzzHBJLp\nFhXvO1a1LyJuAm6q4vPHVXrfKn1dQvKLQVVGR8SlklqS3G2/Ov3M2yQPQlZ7DTMzM7OmpkncYbZ1\n3JIur/cCcG9EvFDogBqCzz//nL333puePXvStWtXLrnkEgCOO+44dtllF4qKiigqKmLu3LkFjtTM\nzMw2tiZzh7kmEVEClFQel/Qc664YAck6yC9lPbekg1i/gcnbETF4A8P82qq4024Z5GuHDTBq1CiO\nOuqoAkdoZmZmtcUFcw0iom/NR9V4jseAx9KH6ZSvGYrVXzW1wzYzM7PGy1MyapmkjpJelfQnkmkQ\nP5P0kqSXJf0x57hj8oyXSvqjpDmSnpC0t6QSSW9JOrya6x4n6T5Jj0p6Q9JVuefM2T4qbU6CpAmS\nbpI0LT3/AZLGp/FP2LiZaXjytcO+8MIL6dGjB7/61a/44osvajiLmZmZNTS+w1w3OgPHA5eTdN3r\nDXwMPC7pSGAWyZSNdcYj4gGSTn4lEXG+pPvTcwwk6Ug4kWTVi3yKSLoIfgEskHR9RCyqIdatge8B\nh5M0UdkPOAl4XlJRRFQ7SbextcauqR32FVdcwfbbb8+qVas4+eST+eMf/8jFF19cwIjNzMxsY3PB\nXDfeiYhnJR1BUvx+CCDpDpJ1oCPP+APAKpI23ZB0//siXV/5JaBjDdd9MiI+Tc/5Csl6zDUVzA9G\nRKTn/6Birrakf6fXW69grtQam4u7r67hEg1HvnWVO3bsyI033sjRRx/NggULAOjVqxd33303/fv3\nr/G8paWlXrM5I+cqG+cpO+cqO+cqG+cpu4aaKxfMdaOiU16+Sa/VTYYty2l8srYTYESsSTsJVid3\nfkA5X/555zZSaZHnM7ldByveV3m9ptAaO1877M6dO9OhQwciggceeIADDjggU8vPhtoatBCcq2yc\np+ycq+ycq2ycp+waaq5cMNet54CxktqRTL04BrieZEpGVeO15QNJXYAFwGCSTohWjXztsL/3ve/x\n4YcfEhEUFRVx8803FzpUMzMz28hcMNehiHhf0gXANJK7yo9ExGSAfOO1ZCTwEMn0jJeBVrV4rUYh\nXzvsqVOnFiAaMzMzq0sumGtZRCwEuuW8vxO4s4rj8o23ytm+NN++Kj43gXW7CA7K2b4HuKeKzxxX\nTdzHVT7ezMzMrCnwsnJmZmZmZtXwHeYGrj51ETQzMzNrjHyHuYGLiMcioqjSj4vljejzzz9n7733\npmfPnnTt2pVLLrlknf0jRoxY2wXQzMzMGh/fYTarQfPmzZk6dSqtWrWirKyMfv36ccghh7DPPvsw\ne/ZsPvnkk0KHaGZmZrWoyd9hltRW0mkb+ZwlkhZImpv+bLcxz291S9LaO8hlZWWUlZUhifLycs49\n91yuuuqqGs5gZmZmDZnvMENb4DTgTxv5vMMiYvZGPmfBSdo0Isrz7W8srbFzW2IDlJeX07t3b958\n801OP/10+vbty9ixYzn88MPp0KFDgaI0MzOzutDk7zADVwKd0jvBo9KflyW9JOloAEnFkqZLul/S\nK5JulrRRcidpgqSbJE2T9JakAySNl/SqpAk5x90kabakf0v6Xc74Qkm/k/RCGvMe6fjekv4l6cX0\ntXM63lLS3yXNl3S3pOck9Un3HShpZnquSZJa5VzjYknPAD/eGN+7odl0002ZO3cu7777LrNmzWL6\n9OlMmjSJESNGFDo0MzMzq2X6suty0ySpI/BQRHSTNAQ4BTgYaAc8D/QFOgOPAnsC76Tbf07XM67q\nnCXAtiTtqO8FLo88iU6L4hYk3f0OB24H9gP+nV7/xIiYK2mbiPhI0qbAk8CZETFf0kLg6oi4Pp1a\n8p2IOEnSVsCKiFgt6QfAqRExRNI5wG4R8QtJ3YC5wD7AQuA+4JCI+EzS+UDziLgsvcafIqLKuQeS\nTgZOBmjXrn3vi8eMqy7lDUL3Hdvk3Tdx4kQAJk+ezOabbw7A4sWL6dChA3fccUem85eWlvpBwYyc\nq2ycp+ycq+ycq2ycp+zqW64GDBgwJyL61HScp2Ssqx9wVzrl4ANJTwF7AcuAWRHxFoCku9JjqyyY\nSaZjvCepNUnB/DPgf6q57oMREZJeAj6IiJfS6/wb6EhS1A5NC9NmQAeS4n1++vn70tc5wI/S7TbA\nREm7AQFslvMdxwJExMuSKs6xT3rOGZIANgdm5sR4d77gI+IW4BaAzp07x4hhR1TzVRueDz/8kM02\n24y2bduycuVKLrroIs4//3xuu+22tce0atWK9957L/M5S0pKKC4uroVoGx/nKhvnKTvnKjvnKhvn\nKbuGmisXzOtSNfsq3yHOe2s+It5LX5dLuhPYm+oL5i/S1zU52xXvm0naBTgH2CsiPs65K1358+V8\n+Wf638C0iBic3kUvScfzfUcB/4yIY/Ls/6ya+Bu1999/n+HDh1NeXs6aNWsYOnQogwYNqvmDZmZm\n1ii4YIblQOt0ezrwC0kTgW2A/sC5wB7A3mnh+g5wNOkd1cokNQPaRsQSSZsBg4AnvmaMW5EUrJ9K\n+gZwCF8WwPm0ASpueR6XM/4MMBSYJmlPoHs6/ixwo6RvR8SbkloC34yI179m7A1ejx49ePHFF6s9\nprS0tI6iMTMzs7rW5B/6i4ilJNMQXgb2JZnmMA+YCpwXEf+XHjqT5AHBl4G3gfvznLI58Fg61WEu\nSdH6tSb1RsQ84EWSec3jgRkZPnYVcIWkGcCmOeN/Atqn8Z1P8n0/jYgPSQrru9J9z5L8omBmZmbW\npPkOMxARP600dG4Vh62IiKMznOszoPcGXPu4nO2FQLc8+46jChHRMWd7NlCcbs8Eds859KL09XPg\n2Ij4XFInkgcI30k/M5Vkznbea5iZmZk1NU3+DnMT1BJ4RtI8krvkp0bEqgLHVFCLFi1iwIABdOnS\nha5duzJ27FgA5s2bx7777kv37t354Q9/yLJlywocqZmZmRWC7zBnEBElVDFnWNJzJFMwcv2sYpWL\nSsdeyPprGE+KiN9vpDDzknQpUBoRoyNiOVDj8ilNSbNmzbj66qv5zne+w/Lly+nduzcDBw7kpJNO\nYvTo0RxwwAGMHz+eUaNG8d///d+FDtfMzMzqmAvmryEi+m7Asb8Har04tg3XoUOHtd36WrduTZcu\nXXjvvfdYsGAB/fv3B2DgwIEcdNBBLpjNzMyaIE/JaKQkXShpgaQnSBqvIKlI0rNpl7/7JW2dju+V\njs2s6HSYjneVNCvtgjg/XdO5UVu4cCEvvvgiffv2pVu3bkyZMgWASZMmsWjRogJHZ2ZmZoXgO8yN\nkKTewE+AXiR/xi+QNDX5H2BERDwl6TLgEuCXwG3AyRHxL0lX5pzqFGBsRNwhaXPWXW2jSivLyuk4\n8uGN+4VqycIrD1vnfWlpKUOGDGHMmDFstdVWjB8/njPPPJPLLruMww8/fG1XPzMzM2tamnxr7MZI\n0i+BbSLi4vT9NcCnJG22d0rHOgGTgO8B8yJi53S8B3Bn2ir8p8CFJIX2fRHxRp7rNcjW2Lntr1ev\nXs0FF1zAXnvtxdChQ9c7dtGiRfzhD3/gpptu2ijXrm+tQesz5yob5yk75yo75yob5ym7+pYrt8a2\nrL8J5e1uGBF3pg82HkaytvRJ6dJzlY9r0K2xI4Lhw4ez3377MWbMmLXjixcvZrvttmPNmjUcd9xx\nnHvuuRutnWdDbQ1aCM5VNs5Tds5Vds5VNs5Tdg01V57D3DhNBwZL2kJSa+CHJJ0CP5a0f3rMz4Cn\nIuJjYLmkfdLxn1ScRNKuwFsRcR0wBehRZ9+gDs2YMYPbb7+dqVOnUlRURFFREY888gh33XUXu+++\nO3vssQc77LADxx9/fKFDNTMzswLwHeZGKCJekHQ3SafBd4Cn013DgZvTttdvARUV4InAOEmfkSyf\n92k6fjRwrKQy4P+Ay+rmG9Stfv36kW9q0llnnVXH0ZiZmVl944K5kapmGbt9qhj7d0T0AJA0Epid\nnuMK4IpaC9LMzMysAXDBbACHSbqA5O/DO8BxhQ3HzMzMrP5wwWxExN3A3YWOw8zMzKw+8kN/1qQt\nWrSIAQMG0KVLF7p27crYsWMBmDt3Lvvssw9FRUX06dOHWbNmFThSMzMzKxQXzI2EpOMk7ZBnX7Gk\nh+o6poagWbNmXH311bz66qs8++yz3Hjjjbzyyiucd955XHLJJcydO5fLLruM8847r9ChmpmZWYF4\nSkbjcRzwMvC/tXUBSc0iYnVtnb8QOnToQIcOHQBo3bo1Xbp04b333kMSy5YtA+DTTz9lhx2q/F3E\nzMzMmgAXzPWYpLOBE9K3twIPAA9FRLd0/zlAK5JCuQ9wh6SVwL7AAcAYYAlJa+yKc24DjAd2BVaQ\ntMSeX834pcAOQMf0XD+tLuaG0Bq7ckvsteMLF/Liiy/St29fxowZw0EHHcQ555zDmjVr+Ne//lXH\nUZqZmVl94dbY9ZSk3sAEkmXgBDwHHAvcXrlgjohLJZUA50TEbEktgDdI2l6/SfJAX8uIGCTpemBJ\nRPxO0veAayKiqJrxS0kan/SLiJV5Ym1QrbFzW2JXWLlyJWeddRbHHnss/fv357rrrqNnz54ccMAB\nTJs2jYceeoirr756o8VQ31qD1mfOVTbOU3bOVXbOVTbOU3b1LVdZW2O7YK6nJJ0FbBsRF6fv/xv4\nkOTOb00FcxFwXUT0T487PP3cIEkvAkMi4q103yKgG0nDkqrGfwVERPwuS9w77frt2GTo2I2ThFpS\n+Q5zWVkZgwYN4qCDDuLss88GoE2bNnzyySdIIiJo06bN2ikaG0NDbQ1aCM5VNs5Tds5Vds5VNs5T\ndvUtV5IyFcyeklF/qYqxtqz7oGaLaj6f7zehqs4b1YxD0lY7ky0225QFeaY81EcRwYknnkiXLl3W\nFssAO+ywA0899RTFxcVMnTqV3XbbrYBRmpmZWSF5lYz6azpwpKSWkrYEBgP/ALaTtK2k5sCgnOOX\nA63T7deAXSR1St8fU+m8wyBZPYNkGsayasYbtRkzZnD77bczdepUioqKKCoq4pFHHmHcuHH8+te/\npmfPnvzmN7/hlltuKXSoZmZmViC+w1xPRcQLkiYAFQsA3xoRz0u6jGQ+89skhXGFCcDNOQ/9nQw8\nLGkJ8AzJ9AqAS4HbJM0nebhveA3jjVq/fv3INy1pzpw5dRyNmZmZ1UcumOuxiLgGuKbS2HXAdVUc\ney9wb87Qo8AeVRz3EXDEBoxfuqFxm5mZmTUmnpJhZmZmZlYNF8zWpLk1tpmZmdXEBbNVSVIfSdel\n28WSvlvomGqDW2ObmZlZTTyH2aoUEbOB2enbYqAUaHTt7twa28zMzGrigrmJkNSRqttqF5OsujGA\nZJ3nEyPi6XRpuXOAM4BTgHJJxwIjIuLpuo6/Lrg1tpmZmVXFBbMBNIuIvSUdClwC/KBiR0QslHQz\nUBoRo2s60cqycjqOfLgWQ/36Knf6g6RV55AhQxgzZgxbbbUVv/3tb7n22msZMmQIf//73znxxBN5\n4oknChCtmZmZFZoLZgO4L32dA3Tc0A9LOplk3WfatWvPxd1Xb7zIakFJSck671evXs0FF1xA3759\n2WabbSgpKWH8+PEMHjyYkpIS2rdvz8yZM9f73NdRWlq6Uc/XmDlX2ThP2TlX2TlX2ThP2TXUXLlg\nbjpWk7+t9hfpazlf4e9ERNwC3ALQuXPnGDFsveWc662IYPjw4ey3336MGTNm7fi3vvUtJFFcXMyT\nTz7JHnvsQXFx8Ua7bklJyUY9X2PmXGXjPGXnXGXnXGXjPGXXUHPlgrnp+IC0rTbJA3yDSJqbZLEc\n2Kq2AiukitbY3bt3p6ioCIA//OEPjBs3jrPOOovVq1fTokULt8Y2MzNrwlwwNxERUVZNW+2aPAjc\nI+kIGtlDf26NbWZmZjVxwdyE5GurnbN/Cekc5ogoAUrS7deBHrUeoJmZmVk95MYlZmZmZmbVcMFs\nZmZmZlYNF8zW5CxatIgBAwbQpUsXunbtytixY9fuu/766+ncuTNdu3Z1O2wzMzMDPIfZmqBmzZpx\n9dVX853vfIfly5fTu3dvBg4cyAcffMDkyZOZP38+zZs3Z/HixYUO1czMzOoBF8xWJUmbRkR5oeOo\nDR06dKBDhw4AtG7dmi5duvDee+8xbtw4Ro4cSfPmzQHYbrvtChmmmZmZ1RMumJsgSR1J1mB+DugF\nvA78F/AKMB44ELhB0vPAjUB7YAXw84iodjm6+toau6p22AALFy7kxRdfpG/fvpx77rk8/fTTXHjh\nhbRo0YLRo0ez11571XGkZmZmVt8o3xq01nilBfPbQL+ImCFpPEmxfAbwp4i4Kj3uSeCUiHhDUl/g\nioj4XhXny22N3fviMePq5otsgO47tllvbOXKlZx11lkce+yx9O/fn+OPP55evXoxYsQIXnvtNS67\n7DLuvPNOJG30eEpLS2nVqtVGP29j5Fxl4zxl51xl51xl4zxlV99yNWDAgDkR0aem41wwN0FpwTw9\nInZK338POBMoAg6IiHcktQI+BBbkfLR5RHSp7tw77frt2GTo2OoOKYjKd5jLysoYNGgQBx10EGef\nfTYABx98MCNHjlzbsrNTp048++yztG/ffqPH01BbgxaCc5WN85Sdc5Wdc5WN85RdfcuVpEwFs6dk\nNF2Vf1OqeP9Z+roJ8ElEFG3ISbfYbFMW5Jn+UF9EBCeeeCJdunRZWywDHHnkkUydOpXi4mJef/11\nVq1aRbt27QoYqZmZmdUHXlau6dpJ0r7p9jHAM7k7I2IZ8LakHwMo0bOOY6wVM2bM4Pbbb2fq1KkU\nFRVRVFTEI488wgknnMBbb71Ft27d+MlPfsLEiRNrZTqGmZmZNSy+w9x0vQoMl/Rn4A3gJmBEpWOG\nATdJ+i2wGfA3YF6dRlkL+vXrR76pSH/961/rOBozMzOr71wwN11rIuKUSmMdc99ExNvAwXUWkZmZ\nmVk95CkZZmZmZmbVcMHcBEXEwojoVug46lq+ltiXXnopO+644zrzmc3MzMwqeEpGEyVpIdAnIpYU\nOpa6kq8lNsCvfvUrzjnnnAJHaGZmZvWRC+YmQFKziFhd6DgKLV9LbDMzM7PqeEpGAyLpIkmvSfqn\npLsknSPp55KelzRP0r2SWqbHTpB0jaRpwB8lbSvpcUkvpitjKOe8x0qaJWmupD9L2jQdL5X0+/Tc\nz0r6RmG++caX2xIb4IYbbqBHjx6ccMIJfPzxxwWOzszMzOoT32FuICT1AYYAvUj+3F4A5gD3RcS4\n9JjLgROB69OP7Q78ICLKJV0HPBMRl0k6jLSVtaQuwNHAfhFRJulPJMvJ/Q+wJfBsRFwo6Srg58Dl\n1cW5sqycjiMf3phf/Wur3OWvtLSUIUOGMGbMGLbaaitOPfVULrroIiRx0UUX8etf/5rx48cXKFoz\nMzOrb1wwNxz9gMkRsRJA0oPpeLe0UG4LtAIey/nMpIgoT7f7Az8CiIiHJVXcRv0+0Bt4Pm3SsQWw\nON23Cngo3Z4DDKwqMEknkxbg7dq15+Lu9Wv2R0lJydrt1atXc8EFF9C3b1+22WabdfYBdO/enTvv\nvHO98Y2ttLS01q/RWDhX2ThP2TlX2TlX2ThP2TXUXLlgbjjytZybABwZEfMkHQcU5+z7rNKxVXXr\nEDAxIi6oYl9ZfNnho5w8f18i4hbgFoDOnTvHiGFH5Am1sCKC4cOHs99++zFmzJi14++///7auc3X\nXnstffv2rfU+9yUlJbV+jcbCucrGecrOucrOucrGecquoebKc5gbjmeAH0pqIakVUDHPoDXwvqTN\nSKZS5DO9Yr+kQ4Ct0/EngaMkbZfu20bSzrXxBQotX0vs8847j+7du9OjRw+mTZvGtddeW+hQzczM\nrB7xHeYGIiKelzSFpDX1O8Bs4FPgIuC5dOwlkgK6Kr8D7pL0AvAU8J/0vK+kra8fl7QJUAacnp6v\nUcnXEvvQQw8tQDT/v727j7OqrPc+/vkqmg8ghkJnlBIr5EGgyYeQIyFIY/mQqGhKlDNKmXeZ3pUY\nZhl6Mse7OJJlmt6i6DEyxRTLg3GQAdN8CETABw7eOR00hRB8GFQS+N1/rGtwM+692cowezbzfb9e\nvPZa17rWWtf+sUZ/s7jW+pmZmVmlcMJcWX4aERPTmzDmAZMiYgFwTcuOEVHXYv1l4Kicpm/lbLsN\nuC3PMTrnLN8B3LG1X8DMzMys0jhhrizXSeoP7EI273hBuQdkZmZmtr1zwlxBIuKL5R6DmZmZWUfj\nh/5su7Z8+XJGjBhBv379OPDAA/nZz34GwA9+8AMGDRpEdXU1Rx11FH//+9/LPFIzMzNrr5wwb+ck\n9ZK0JE/7pZI+s4V9J0o6f9uNbtvr1KkTkyZN4umnn+bhhx/m6quv5qmnnmL8+PEsWrSIhQsXctxx\nx3HppZeWe6hmZmbWTnlKRgcVEReXewxtoaqqatM7lrt06UK/fv144YUX6N+//6Y+a9euJRVtMTMz\nM3sXJ8wdw46Srgf+FXgBGEX2Zo3fR8Qdko4B/h1YRVZy+6MRcVzat7+kBuAjwOSIuKrYidpLaeyW\n5bABGhsbefzxxxk8eDAAF110ETfffDNdu3Zlzpw5bT1EMzMzqxDK915a235I6gU8CxwSEQsl/RaY\nAXyGrOz174FlwLCIeE7SNKBLRBwnaSLZq+hGkL3feSnwLxHxdotz5JbGPvjiyde3xVcrauC+XTdb\nf/PNNznvvPP40pe+xLBhwzbbduutt/LPf/6TM844o83G19TUROfOnbfc0RyrEjlOpXOsSudYlcZx\nKl17i9WIESPmR8QhW+rnO8wdw3MRsTAtzwd65WzrC/w1Ip5L69NIyW/yh4hYB6yTtBL4EPB87sHb\ne2nst99+m+OOO46zzz6bb3/72+/avv/++3PssccyderUNhtTpZYGLQfHqjSOU+kcq9I5VqVxnEpX\nqbHyQ38dw7qc5Q1s/ovSlibvFtu33YsIxo0bR79+/TZLlpctW7ZpecaMGfTt27ccwzMzM7MKUFHJ\nj20TzwAfldQrIhqBU8s8nlb14IMPcssttzBw4ECqq6sB+PGPf8wNN9zA0qVL2WGHHdhvv/249tpr\nyzxSMzMza6+cMHdwEfGmpK8DMyWtAh4t95ha09ChQ8k3T/+YY44pw2jMzMysEjlh3s6lu8YDctZ/\nmqfbnIjoq+zdalcDf0l9J7Y41oA8+5qZmZlt1zyH2QC+Kmkh8CTQFfhVmcdjZmZm1m44YTYi4sqI\nqI6I/hExNiLeKPeYWkOhstjjx4+nb9++DBo0iBNPPJFXXnmlzCM1MzOz9swJs223CpXFrqmpYcmS\nJSxatIgDDjiAyy+/vNxDNTMzs3asQyTMkvZMD7a15jFnSnpC0pOSrpW0Y2rvJmmWpGXp84OteV4r\nXVVVFQcddBCweVnso446ik6dsun7hx12GM8//3yxw5iZmVkH1yESZmBPoFUTZuALEfEJsgfqugOn\npPYJwOyI6A3MTuvbBUkV+5Boy7LYzaZMmcLRRx9dplGZmZlZJajYBOg9qgc+lh5sm5XajgYC+FFE\n3CZpOHBQ+e0JAAAgAElEQVQp8DLQB5gHfD0iNuY7YES8lhY7ATunYwGMAoan5alAA/DdfMdIpaf3\nB6qAA4BvA4elsb0AfD4i3pZ0MfB5YFfgIeBrERGSGoBHyEpX7wmMi4gHUjnsW4Dd06nOiYiHJO0A\n/AI4AniO7BemKRFxh6SDgX8HOgOrgLqIeDGd4yHgcLKS2pPyfZdmb769gV4T/lCsyzbXWH/sZutN\nTU2MHj2ayZMns8cee2xqv+yyy+jUqRNjx45t6yGamZlZBVG+d9Rub1IC+fuIGCBpNHA28Dlgb+Ax\nYDBZkjwT6A/8LS3/KiLuKHLc+4BPAf8JfDkiNkh6JSL2zOmzJiLyTstICfNnyBLe/sCfgdER8Z+S\nfgdMjYi7JHWLiNVpn1uA30bEPSmZnR8R35F0DPDtiPiMpN2AjRHxlqTewLSIOETSycCZwHFAD+Bp\n4KvA3cBcYFRE/EPSqcBnI+LMdI6nIqLgHXpJZ5HKae+9d/eDL558faGubWLgvl03La9fv54LL7yQ\nQw89lC984Qub2mfOnMk999zDpEmT2GWXXdp8jE1NTXTu3LnNz1uJHKvSOE6lc6xK51iVxnEqXXuL\n1YgRI+ZHxCFb6tdR7jDnGkqWQG4AVkiaCxwKvAY8GhF/BZA0LfUtmDBHxGcl7QLcChzJO3ev34v/\nTHeRFwM7kiXqAIuBXml5hKQLgN2AbmSvf7snbbszfc7P6b8T8AtJ1WTlrA/I+e63p7vmL0mak9r7\nkE0tmZW9ipkdgRdzxnhbsS8QEdcB1wH06dMnvjl2VElffFuLCGprazn88MOZPHnypvaZM2cyY8YM\n5s6dS/fu3csytoaGBoYPH16Wc1cax6o0jlPpHKvSOValcZxKV6mx6ogJs4psa3m7fYu339Nd3Blk\nUzFmkSXhVWk6QxWwcguHWJeOs1HS2/HOLf+NQKeUkP8SOCQilqe70ru03J8sMW7++/wWsAL4BNm0\ni7dSe6HvLuDJiBhSYPvaLXyHdqlQWexzzz2XdevWUVNTA2QP/rk0tpmZmRXSURLm14EuaXke8DVJ\nU8nu1g4DxgN9gU9J2p9sSsappLumLUnqDHRJSXEn4BjggbR5BlBLNm+6lmy6w9ZoTo5XpfOeTJG7\n3klX4PmUhNeS3TEG+BNQm757d7K51r8GlgLdJQ2JiD9L2gk4ICKe3Mqxl5XLYpuZmVlr6BAJc0S8\nLOlBSUvI5hsvAp4gu4N8QUS8JKkv2RziemAgWWL9uwKH3B2YIekDZMno/UDzLcp64LeSxgH/wztv\nz3i/Y39F0vVkUzQayeZcb8kvgemSTgHm8M4d4unASGAJ8N9kDwy+GhH/TPObr5LUley6mEw29cPM\nzMysQ+sQCTNARHyxRdP4PN3eiIhTSzjWCrJ5z/m2vUyWlJYypokt1jvn2xYR3we+n2f/4TnLq0hz\nmCNiGTAop+uFqX2jpPMjoknSXsCjZIk4EbGQ7G57wXOYmZmZdUQd5T3M9o7fp9frPQD8W0S8VO4B\ntaZC5bBXr15NTU0NvXv3pqamhjVr1pR5pGZmZlYpnDAnEdEQEce1bJf0iKSFLf4MfC/HlnRGnmNc\n3XqjL11EDI+I6ojoHxE3lWMM21Khctj19fWMHDmSZcuWMXLkSOrr68s9VDMzM6sQHWZKxvsVEYO3\n3GuLx7gRuLEVhmNbUFVVRVVVFbB5Oey7776bhoYGAGpraxk+fDhXXHFFGUdqZmZmlcJ3mNsRSadL\nWiTpCUm3SNpP0uzUNlvSR1K/myRdI2mOpL9KOkLSFElPS7op53hNkq6QNF/Sf0n6lKSGtM/xqc8u\nkm6UtFjS45JGpPY6SXdKmilpmaT/U5agbIXcctgrVqzYlEhXVVWxcuWW3vZnZmZmlvEd5nZC0oHA\nRcDhEbFKUjey0to3R8RUSWcCVwEnpF0+SFYs5XiyIiaHA18BHpNUnR7i2x1oiIjvpsqBPwJqyKoK\nTiV7Bd43ACJiYHpTyB8lNRc6qQY+Sfau56WSfh4Ry4t9j3KVxi61HLaZmZnZe+WEuf04Ergjve2C\niFgtaQhwUtp+C5B7l/eeiIhUIXBFRCwGkPQk2dsyFgL/ZPPKgetyqgr2Su1DgZ+ncz4j6W+8Uxlw\ndkS8mo77FLAf8K6EuUVpbC4euH5r4vC+NE+3gHfKYQ8ePJhu3brR0NDAHnvswfTp09lrr714+eWX\n6dKly2b7tLWmpqaynr+SOFalcZxK51iVzrEqjeNUukqNlRPm9kNsubJg7vbmCn8bc5ab15v/XltW\nDsytKtjcp1jlw9zj5lYS3HxQ7ag0dqFy2KeeeirLli1j9OjR1NfXc9ppp5W1NGellgYtB8eqNI5T\n6Ryr0jlWpXGcSlepsfIc5vZjNvCF9H5k0pSMh4DT0vaxZJX6Wtu8dGzSVIyPkFX+q0jN5bDvv/9+\nqqurqa6u5t5772XChAnMmjWL3r17M2vWLCZMmFDuoZqZmVmF8B3mdiIinpR0GTBX0gbgceBcYIqk\n8cA/gDO2wal/CVybpmmsB+oiYp1U7MZz+1WoHDbA7Nmz23g0ZmZmtj1wwtyORMRUsofxch2Zp19d\nznIjMKDAtryVA3O3RcRbQB0tpHc035Sz/q53VJuZmZl1BJ6SYWZmZmZWhBNmMzMzM7MinDDbdmX5\n8uWMGDGCfv36ceCBB/Kzn/0MgNWrV1NTU0Pv3r2pqalhzZo1ZR6pmZmZVQonzO2QpImSzi/3OCpR\np06dmDRpEk8//TQPP/wwV199NU899RT19fWMHDmSZcuWMXLkSOrr68s9VDMzM6sQTpi3UznvWe5Q\nqqqqOOiggwDo0qUL/fr144UXXuDuu++mtrYWgNraWu66665yDtPMzMwqSIdMqtojSRcBp5NV0vsH\nMF9SNXAtsBvw/4AzI2JNkfYGsnc3Hw7MkDQQeBPoS1al7wygFhgCPNL8Rg1J1wCHAruSVRv8YWpv\nJHtrx+eBnYBTIuKZYt+jvZTGBmhsbOTxxx9n8ODBrFixgqqqKiBLqleuXNnWQzQzM7MK5TvM7YCk\ng8kKlHySrBT2oWnTzcB3I2IQWWnrH26hHWDPiDgiIial9Q+SvZruW8A9wJXAgcDAlHgDXBQRhwCD\ngCMkDco53qqIOAi4BqiYaSJNTU2MHj2ayZMns8cee5R7OGZmZlbBfIe5ffg08LuIeANA0gxgd7Lk\nd27qMxW4XVLXfO05x7qtxbHviYhIhUlWRMTidI4ngV7AQrIKg2eRXQ9VQH9gUdr/zvQ5nyyZf5e0\n71kAe+/dnYsHrn+PX3/r5dalX79+PRdeeCGDBw+mW7duNDQ0sMceezB9+nT22msvXn75Zbp06VLW\nWvZNTU1lPX8lcaxK4ziVzrEqnWNVGsepdJUaKyfM7Uf+8nTv3doW6+vS58ac5eb1TpL2J7tzfGia\n1nETsEue/TdQ4HqJiOuA6wD69OkT3xw7aqu+wNaICGprazn88MOZPHnypvZTTz2VZcuWMXr0aOrr\n6znttNPKWsu+oaGhrOevJI5VaRyn0jlWpXOsSuM4la5SY+UpGe3DPOBESbtK6kI2Z3gtsEbSp1Of\nLwNzI+LVfO1bce490rlelfQh4OitOFbZPfjgg9xyyy3cf//9VFdXU11dzb333suECROYNWsWvXv3\nZtasWUyYMKHcQzUzM7MK4TvM7UBELJB0G9n0iL8BD6RNtcC1knYD/kr20F6x9vdz7ickPQ48mY71\n4Ps9VnswdOhQIvLfrJ89e3Ybj8bMzMy2B06Y24mIuAy4LM+mw/L0XVigfXiL9bqc5UZgQIFtdeQR\nEb1ylv8CDM/Xz8zMzGx75ikZZmZmZmZFOGG27caZZ55Jjx49GDBg0410nnjiCYYMGcLAgQP5/Oc/\nz2uvvVbGEZqZmVklcsJs2426ujpmzpy5WdtXvvIV6uvrWbx4MSeeeCI/+clPyjQ6MzMzq1QdImGW\ntKekr7fi8XaT9AdJz0h6UlJ9zrYPSLpN0rOSHpHUq7XOa8UNGzaMbt26bda2dOlShg0bBkBNTQ3T\np08vx9DMzMysgnWIhBnYE2i1hDn5aUT0JavOd7ik5texjQPWRMTHyarqXdHK5y0bSRX3kOiAAQOY\nMWMGALfffjvLly8v84jMzMys0lRcAvQ+1QMfk7QQmJXajiYrFvKjiLhN0nDgUuBloA/Zu5G/HhEb\nWx4sVeSbk5b/KWkB0DNtHgVMTMt3AL+QpMjzrjNJdcAJwI5kb7CYBOxM9m7ldcAxEbFa0lfJKunt\nDDwLfDki3khFRl4DDgH+BbggIu6Q1Bm4m6ws9k7A9yPi7nTOHwBjgeXAKmB+RPxU0seAq4HuwBvA\nVyPimXSO1WS/GCwAvlMs0G++vYFeE/5QrEuraqw/tuj2KVOmcO6553LppZdy/PHHs/POO7fRyMzM\nzGx70VES5gnAgIioljQaOBv4BLA38Jikeanfp8jKQv8NmElWCvqOYgeWtCdZoZGfpaZ9yZJRImK9\npFeBvciS03wGkCWju5Alw9+NiE9KuhI4HZgM3BkR16fz/YjsLvbP0/5VwFCgLzAjjfct4MSIeE3S\n3sDDqdz2wcDodL5OZAnw/HSc64CzI2KZpMHAL4Ej07YDgM9ExIYCMShbaeyW5TVfeukl1q5du1n7\n9773PQCWL19Ojx492kVJzkotDVoOjlVpHKfSOValc6xK4ziVrlJj1VES5lxDgWkp+VshaS5wKNmd\n2kcj4q8AkqalvgUT5jRFYRpwVfN+gPJ0LVb2ek5EvA68npLre1L7YmBQWh6QEuU9gc7AfTn735Xu\ng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0REP+Aw4BspHr6m3q1QrMDXVUvrgCMj4hNANfA5SYcBV5DFqjewBhiX+o8D1kTEx4Er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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a241d7f98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 特征选择结果 XGBClassifier 的 feature_importance\n",
    "fig, ax = plt.subplots(figsize=(10, 8))\n",
    "xgb.plot_importance(xgb_c.fit(select_X_train, y_train), ax = ax)\n",
    "#plt.yticks(np.arange(len(vector_names)), select_features_sort[0])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a186096a0>"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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OzMzMrFHylgwzMzMzswKcMJuZmZmZFeCE2czMzMysACfMZmZmu+GDDz5g0KBB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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a0abc2278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# xgb 的 feature_importance\n",
    "param = xgb_c.get_xgb_params()\n",
    "param['num_class'] = 3\n",
    "xgb1 = xgb.train(param, xgb.DMatrix(select_X_train, label = y_train), 10)\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(10, 12))\n",
    "xgb.plot_importance(xgb1, ax = ax)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "xgb的 feature_importance 和 XGBClassifier 的 feature_importance 结果不一样？"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 个特征在测试集上的评价结果 0.66\n",
      "1 个特征在测试集上的评价结果 0.64\n",
      "2 个特征在测试集上的评价结果 0.605\n",
      "3 个特征在测试集上的评价结果 0.62\n",
      "4 个特征在测试集上的评价结果 0.65\n",
      "5 个特征在测试集上的评价结果 0.615\n",
      "6 个特征在测试集上的评价结果 0.68\n",
      "7 个特征在测试集上的评价结果 0.685\n",
      "8 个特征在测试集上的评价结果 0.655\n",
      "9 个特征在测试集上的评价结果 0.645\n",
      "10 个特征在测试集上的评价结果 0.635\n",
      "11 个特征在测试集上的评价结果 0.665\n",
      "12 个特征在测试集上的评价结果 0.67\n",
      "13 个特征在测试集上的评价结果 0.665\n",
      "14 个特征在测试集上的评价结果 0.665\n",
      "15 个特征在测试集上的评价结果 0.665\n",
      "16 个特征在测试集上的评价结果 0.65\n",
      "17 个特征在测试集上的评价结果 0.63\n",
      "18 个特征在测试集上的评价结果 0.66\n",
      "19 个特征在测试集上的评价结果 0.66\n",
      "20 个特征在测试集上的评价结果 0.665\n",
      "21 个特征在测试集上的评价结果 0.64\n",
      "22 个特征在测试集上的评价结果 0.66\n",
      "23 个特征在测试集上的评价结果 0.645\n",
      "24 个特征在测试集上的评价结果 0.665\n",
      "25 个特征在测试集上的评价结果 0.66\n",
      "26 个特征在测试集上的评价结果 0.66\n",
      "27 个特征在测试集上的评价结果 0.655\n",
      "28 个特征在测试集上的评价结果 0.66\n",
      "29 个特征在测试集上的评价结果 0.67\n",
      "30 个特征在测试集上的评价结果 0.64\n",
      "31 个特征在测试集上的评价结果 0.66\n",
      "32 个特征在测试集上的评价结果 0.67\n",
      "33 个特征在测试集上的评价结果 0.675\n",
      "34 个特征在测试集上的评价结果 0.65\n",
      "35 个特征在测试集上的评价结果 0.66\n",
      "36 个特征在测试集上的评价结果 0.675\n"
     ]
    }
   ],
   "source": [
    "# 将feature name 和 feature importance 合并 并排序\n",
    "xgb_c_feat_imp = xgb_c.feature_importances_\n",
    "select_features = np.vstack((np.array(vector_names, dtype = object),np.asarray(xgb_c_feat_imp,object)))\n",
    "select_features_sort = select_features[:,select_features[1,:].argsort()[::-1]]\n",
    "\n",
    "# 用选择后的特征训练模型并在测试集评价结果\n",
    "selected_feature = select_features_sort[0]\n",
    "for i in range(len(selected_feature)):\n",
    "    xgb_c.fit(X_train[selected_feature[:i]], y_train)\n",
    "    print (i, '个特征在测试集上的评价结果', xgb_c.score(X_test[selected_feature[:i]], y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "使用36或37个特征时，结果并没有变好，但没有差很多，或许只用这37个特征也可以进行很好的预测？"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 生成测试结果文件"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "prediction :  [2 2 2 ..., 2 1 2]\n"
     ]
    }
   ],
   "source": [
    "# 用训练好的模型预测结果\n",
    "pred = xgb_c.fit(select_X_train, y_train).predict(test[select_features[0]])\n",
    "print ('prediction : ', pred)\n",
    "\n",
    "#生成测试文件\n",
    "pd.DataFrame(pred).to_csv('prediction.csv')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
